A Study of the Social Identity of Electric Vehicle Consumers from a Social Constructivism Perspective

Abstract

The present study adopts the social constructivism theory and consumer decision-making process model with the aim of examining the social identity that consumers build through the purchase of electric vehicles (EVs) in line with their income, age, gender, and education. The study’s findings indicate that this social identity, shaped by income, age, gender and education, exerts a significant influence on consumer decision-making behavior. This identity is shaped not only by the make and model of EVs chosen, but also by their preferences for vehicle performance and technical features. The adoption of EVs by consumers is driven by dual objectives: the fulfilment of practical needs and the shaping of social identities in social interactions that correspond to their income, age, gender, and education. The study’s findings are of significant value in understanding the social identity aspirations of consumers in the electric vehicle consumer market, and provide a theoretical foundation for future electric vehicle companies to create products and corporate cultures that meet their target customers, thereby effectively promoting the popularization of electric vehicles.

1. Introduction

A review of global CO₂ emissions by country in 2022 revealed a general upward trend in emissions over the past few decades, with a slowdown in the growth rate observed in recent years. In 2021, total global CO₂ emissions reached approximately 36 billion tons, representing an increase of around 5% compared to 2020 [1]. In the context of intensifying global climate change and escalating concerns over environmental pollution, the transition from conventional vehicles to electric vehicles (EVs) has emerged as a pivotal strategy to mitigate localized air pollution and greenhouse gas emissions within the transportation sector [2]. The market demand for electric vehicles is a subject that has attracted considerable attention from researchers. China, the world’s largest market for electric vehicles, has made significant progress in the promotion and adoption of electric vehicles in recent years. A new study by S&P Global suggests that the country will account for the majority of sales with a 29.7% share [3]. It is anticipated that sales of electric vehicles (EVs) in China will exceed those of internal combustion engine vehicles for the first time next year, marking a significant historical turning point. This is expected to result in domestic EV sales increasing by approximately 20 percent annually, reaching over 12 million units by 2025. Conversely, sales of conventionally-powered vehicles are predicted to decline by more than 10 percent in the same period, reaching below 11 million units. This represents a decrease of nearly 30 percent from the 14.8 million units sold in 2022 [4].

China represents the world’s largest market for electric vehicles. Factors influencing this include not only climate policy, but also industrial policy aligned with global market aspirations (Meckling and Nahm, 2019) [5]. For example, developing economies like China are heavily investing in electric vehicles (EVs) and batteries to break into the global automotive market, as entering the petrol engine vehicle market is difficult due to the dominance of established carmakers from developed economies such as Europe, the U.S., and Japan (Campbell et al., 2023) [6]. In October 2020, the State Council of the People’s Republic of China released the New Energy Vehicle Industrial Plan 2021–2035 aims to build a green, robust, and internationally competitive auto industry in China [7]. The advancement of China’s industrial policy has precipitated the implementation of protectionist measures in Western economies. The United States and the European Union have recently imposed tariffs of up to 102.5% and 48%, respectively, on Chinese electric vehicles. These tariffs were imposed as a response to concerns that state subsidies distort market fairness (Wingrove, 2024, Nardelli and Valero, 2024) [8,9]. These actions are indicative of the historical tensions that have been present in the solar industry, where the predominance of Chinese entities has had a significant impact on the global supply chain infrastructure (Dua, 2024) [10]. In response to these developments, Chinese manufacturers are diversifying their production into developing economies such as Mexico, Brazil, and Thailand (Sebastian, 2024; White et al., 2024) [11,12]. This strategic move is intended to avoid the imposition of tariffs and to establish localized production centers.

As electric vehicles continue to evolve, it is inevitable that a culture analogous to that of traditional automobiles will emerge. The accelerated adoption of electric vehicles (EVs) signifies not solely a technological transition, but also a socio-cultural transformation, wherein consumers’ purchasing decisions are progressively influenced by their social identity and collective values (Schuitema et al., 2013) [13]. From a social constructivism perspective, consumer behavior is co-constructed through social norms, symbolic interactions, and shared meanings (Berger and Luckmann, 1966) [14]. Within this theoretical framework, the ownership of electric cars transcends mere functional utility. The prevailing scholarly consensus posits that consumers’ ownership and utilization of automobiles is typically associated with their instrumental, hedonic, and symbolic attributes (Anable and Gatersleben, 2005, Bergstad, 2011, Steg, 2005, Steg, 2001) [15,16,17,18]. A subset of scholars have argued that the instrumental, hedonic, and symbolic attributes of electric vehicle adoption have influenced the adoption and use of electric vehicles (Heffner, 2006, Heffner, 2007, Kurani, 2007, Skippon and Garwood, 2011, Schuitema, 2013) [13,19,20,21,22].

This study employs social constructivism and the consumer decision-making process model to investigate how consumers’ social identities influence their willingness to purchase electric vehicles by regulating brand image and perception. This area has not been sufficiently explored in existing literature. This study posits that the propensity to acquire electric vehicles is not exclusively determined by brand attributes but is also a process of social identity construction, wherein identity-driven brand perception functions as a pivotal conduit. This paper examines electric vehicle consumers from a social constructivist perspective, emphasizing how consumers shape their unique consumption identities to influence their willingness to purchase electric vehicles. As the world’s largest electric vehicle market with the most mature consumer base, research on the social identities of Chinese electric vehicle consumers can inform similar studies in other countries and regions.

2. Literature Reviews

The present study examines the relationship between brand image, brand perception, and the purchase intention of electric vehicle consumers. It explores the mediating role of brand perception in brand image, and the moderating role of electric vehicle consumers’ social identities (gender, age, education, monthly income, and occupation) in the process of brand image affecting purchase intention. Figure 1 illustrates this process. The influence of social identity on brand perception is not merely passive; rather, it is actively constructed through semiotic labor (Escalas and Bettman, 2005; Reed et al., 2012) [23,24]. The function of a brand is shaped by the process of meaning creation, which is driven by identity. This process involves the assimilation of semiotic clay into self-narratives or its discarding as irrelevant or threatening (Arnould and Thompson, 2005; Chernev et al., 2011) [25,26]. The salient issue is not the brand’s self-perception, but rather the identity narrative it embodies (Ahuvia, 2005) [27]. Elliott and Watanasuwan (1998) posit that brands should not be regarded as mere material objects or functional commodities, but rather as symbolic resources that consumers employ to communicate their self-identity to others [28]. Research by Hackbarth and Madlener (2016) and He et al. (2018) indicates that social identity factors, including gender, age, income, and education, influence the intention to purchase electric vehicles [29,30]. High-income groups may prioritize the symbolic value of a brand, such as its association with social status.In the context of electric vehicle (EV) consumption, social constructivist theory reveals that consumer behavior is not merely an economic decision but also a social practice of identity construction. The core concept of “identity labor” is evident here, as consumers actively participate in creating social meaning and shaping their identities by choosing EVs as tangible commodities. By demonstrating their dedication to environmental responsibility through their product choices, consumers embody the narrative of being “green citizens”. Consumers who possess a pronounced environmental awareness often seek to affirm their “green identity” through their purchasing behavior [13,31]. Consumers establish a social identity that aligns with their personal values by acquiring electric vehicles. During the purchasing process, consumers assess the extent to which the brand image of electric vehicles aligns with their own social identity. Consumer perception is not merely passive reception; rather, it is an active process of reshaping through identity labor (Kates, 2004) [32]. Repeat purchases have been shown to be indicative of identity maintenance, reflecting a ritualistic reinforcement of the self-concept (Schembri et al., 2010) [33]. When a brand neglects to evolve in tandem with its identity narrative, a sense of betrayal emerges (Lam et al., 2010) [34].

2.1. Conceptual Foundations of Brand Image and Perception

Brand image is defined as consumers’ psychological associations and beliefs about a brand (Keller, 1993) [35]. The concept under discussion encompasses not only functional attributes such as quality and reliability but also symbolic attributes such as emotional resonance and social status (Park et al., 1986) [36]. A robust brand image has been demonstrated to have a significant impact on consumer behavior. Specifically, it has been shown to enhance consumer trust, thereby reducing perceived risk. This, in turn, has been found to directly increase the likelihood of purchase (Delgado-Ballester and Munuera-Alemán, 2001) [37]. For instance, studies demonstrate that a corporation’s substantial social responsibility, exemplified by philanthropic contributions, can influence consumer perceptions of its products. This phenomenon suggests that consumers regard products from enterprises engaged in prosocial endeavors as superior(Chernev and Blair, 2015) [38]. This perception constitutes a brand perception of the company’s products. Brand perception is defined as consumers’ subjective evaluations of brand attributes, which include functional, emotional, and social dimensions (Aaker, 1997) [39]. The functional dimension encompasses attributes such as cost-effectiveness, while the emotional dimension includes brand likability. The social dimension relates to consistency with personal identity. These perceptions elicit emotional responses, thereby fostering brand loyalty and encouraging repeat purchases. In this regard, brand attachment serves as an important mediator between positive brand perceptions and purchasing behavior. Empirical evidence suggests that brands that resonate with consumers on an emotional level, such as Nike’s “Just Do It” campaign, can enhance consumer attachment. Consumers with a strong attachment to a brand are less likely to use competing brands. The brand Nike is perceived more as an identity-based rather than a utilitarian brand (Park et al., 2010, Carroll and Ahuvia, 2006) [40,41]. A similar phenomenon has been observed in the context of nostalgia-driven brand marketing, wherein consumers exhibit a pronounced preference for aesthetic products that are associated with their youth. This phenomenon is mediated by successfully satisfying the need for belonging and self-continuity (Muehling and Sprott, 2004, Loveland, 2010) [42,43].

Brand image exerts a significant influence on consumers’ perceptions of product quality and functional value, directly impacting their purchase intentions. According to Zeithaml (1988), consumers have a tendency to associate strong brand images with reliability and performance, which in turn has a fundamental influence on their willingness to pay [44]. The impact of brand image on purchase intention is primarily achieved by enhancing brand credibility and reducing perceived risk(Gürhan-Canli and Batra, 2004, Erdem and Swait, 2004) [45,46].

Brand image and perception influence purchase intentions through cognitive trust, emotional resonance, and social identity mechanisms. These mechanisms are moderated by cultural and digital environments. Consumer social identity, a concept rooted in social psychology and consumer behavior research, refers to the self-concept and sense of social belonging constructed by individuals through consumption behaviors. Belk’s (1988) seminal work argues that consumption is not only a means to satisfy material needs, but also an important mechanism for self-expression, identity construction, and social integration [47]. This perspective aligns with the symbolic interactionist viewpoint proposed by Solomon (1983), which posits that individuals utilize consumption as a means to manage social impressions and convey their desired identities to others. The subjective experience of consuming various products has been shown to contribute substantially to the construction of consumers’ social realities, self-concepts, and behaviors [48]. The process of identity construction through consumption is particularly evident in brand relationships. Escalas and Bettman (2005) posit that consumers often use brands as symbolic resources for expressing self-concept and group affiliation [49].

Brand perception is influenced by the affiliation of the group and social norms. Lam et al. draw on social identity theory to define consumer–brand identification (CBI) as a consumer’s psychological state of perceiving, feeling and valuing their belonging to a brand. The findings of Zhang and Shavitt demonstrate that modernity and individualism predominate as values in contemporary Chinese advertising. Furthermore, research has demonstrated that product characteristics, such as personal use versus shared products, influence the extent to which individualistic and collectivist values are reflected in advertisements (Lam et al., 2013, Zhang and Shavitt, 2003) [50,51]. The effect is further amplified by social media, as evidenced by longitudinal engagement analysis, which demonstrates that consuming user-generated brand-related social media communication (UGC) influences consumers’ mindset, thereby affecting their behavior (Schivinski et al., 2016) [52]. Consumers tend to select brands that align with their self-concept or ideal identity. Empirical research indicates that consumers purchase brands in part to construct their self-concept and, in the process, to form connections to the brand (Escalas and Bettman, 2005) [49]. Luxury brands, such as Louis Vuitton, employ strategic techniques to take advantage of the concepts of scarcity and distinctiveness in their marketing efforts. These techniques are designed to convey a message of social status, effectively addressing the tension between uniqueness and belonging that consumers face. Berger and Heath’s research further indicates that consumers are more likely to diverge from the majority or members of other social groups in product categories perceived as identity symbols (Han et al., 2010, Berger and Heath, 2007) [31,53].

2.2. Social Identity Construction Through Consumption

An examination of historical perspectives on consumer social identity reveals that they can be traced back to Veblen’s (1899) theory of conspicuous consumption. According to this theory, individuals use luxury goods as a sign of social status and economic power [54]. This concept has been further elaborated in contemporary consumer research, wherein scholars have examined how diverse product categories function as identity markers. For instance, Griskevicius et al. (2010) demonstrated that eco-friendly products signal pro-social orientation and environmental awareness, especially in societies that promote such values [55]. The dynamic nature of consumers’ social identities is increasingly evident in the context of modern consumer culture. Cherrier and Murray (2007) emphasized how the fragmentation of traditional social structures and the proliferation of consumer choices have led to more fluid and multifaceted identity constructions [23].

The social constructivism perspective provides a valuable lens for understanding the socio-cultural dimensions of EV consumption. Despite these advances, significant gaps persist in our understanding of the processes of social identity construction associated with EV consumption. Heffner et al. (2007) demonstrated how EV ownership contributes to the construction of “green” identities and participation in environmentally conscious social networks [19]. Axsen and Kurani (2012) further advanced this line of research by introducing the concept of “symbolic meaning making” in the adoption of electric vehicles, emphasizing how consumers use these vehicles to communicate personal values and social belonging [56]. Previous studies have examined the impact of demographic variables (such as gender, age, income, and education level) on the willingness to purchase electric vehicles. These studies include those by Hackbarth and Madlener (2016) and He et al. (2018) [29,30]. Notwithstanding these advances, significant gaps persist in our understanding of the processes of social identity construction associated with EV consumption.

3. Research Objective, Model, and Hypotheses

Figure 2 shows the logical framework diagram for this section.

3.1. Research Objective

This study will verify that consumers have a process of constructing their social identity in purchasing electric vehicles through the social constructivism theory. The study examines consumers’ construction of a social identity that is consistent with their income, age, gender, and education through the purchase of electric vehicles (EVs). This social identity, shaped by income, age, gender, and education, influences their consumption decision behavior.

3.2. Consumer Decision-Making Process Model

All marketing decisions are predicated on assumptions regarding consumer behavior. In this paper, an analysis is conducted of the social identity of consumers and its influence on their purchasing behavior of electric vehicles. The model of Neal et al. (2000) (see Figure 3) is utilized [57], as it has been employed in previous studies on high-involvement purchase decision-making processes (Okumus and McKercher, 2007) [58]. The model has been demonstrated to be effective in elucidating consumers’ cognitive and behavioral trajectories in intricate decision-making processes, particularly in scenarios characterized by high involvement and risk, such as the selection of electric vehicles. It has also been utilized to examine the influence of individualistic-collectivistic cultural dimensions on consumers’ high-involvement product decision-making processes, with a particular focus on automobile purchase decisions (Tahmid and Nayeem, 2012) [59]. Furthermore, this paper integrates the high- and low-involvement decision theory proposed by Belch G. and Belch M. (2018) to deepen the understanding of consumer behavior. The theory posits that high-involvement decisions (electric car purchases) necessitate the allocation of substantial cognitive resources by consumers to mitigate perceived risk through multi-stage information processing and rational trade-offs. In contrast, low-involvement decisions (everyday consumer goods choices) predominantly rely on habitual or heuristic strategies [60]. In this theoretical framework, consumers are conceptualized as active problem solvers and information processors. Their decision-making process is not only based on the evaluation of functional attributes, but also dynamically moderated by social identity (occupation, income, cultural background). For instance, high-income groups may prioritize the symbolic value of a brand, such as its association with social status. Conversely, environmentally conscious consumers tend to reinforce their “green identity” through their purchasing behavior (Schuitema et al., 2013, Han et al., 2010) [13,31].

It is noteworthy that the model demonstrates the potential influence of psychological variables, such as social and cultural influences, on consumers’ attitudes and needs during the purchasing decision-making process (Anurit, Newman, and Chansarkar, 1998) [61]. Research by Belch G. and Belch M. (2018) underscores the significance of considering external factors, including culture, social class, reference groups, and situational determinants, in consumer decision-making processes. Psychological variables such as social and cultural factors align well with social constructivism, the theoretical foundation of this study [60]. Consequently, social constructivism and Neal et al.’s model of consumer decision-making offer a more comprehensive explanation of the social identity construction process among electric vehicle consumers. The incorporation of cultural influences within the model renders it a valuable instrument for cross-cultural behavioral research, particularly in East–West comparisons (Okumus et al., 2007) [58]. However, it is crucial to note that the application of this model to real consumer contexts necessitates a examination of consumer behavior within the context of the actual consumer situation. It is noteworthy that not all consumers necessarily engage in all stages of the model when making a purchase decision. In fact, depending on the nature of the purchase, certain stages may be bypassed. (Panwar, Diksha, et al., 2019) [62].

Social constructivism posits that consumer behavior is co-constructed through social norms, symbolic interactions, and shared meanings (Berger and Luckmann, 1966) [14]. When applied to the study of consumer decision making, this theory emphasizes the role of the social environment in influencing each stage of the decision-making process, which includes problem recognition, information search, evaluation, purchase, and post-purchase.

3.3. Research Hypothesis

In consideration of the research objectives of this paper and the model of the consumer decision-making process, this study will take “consumer’s social identity” as a moderator, and explore how it interacts with brand image and brand perception to further influence consumers’ willingness to purchase electric vehicles. Social constructivism posits that consumers employ brands as a means of expressing identity (Schuitema et al., 2013) [13]. Our identity is constructed out of the discourses culturally available to us. For instance, factors such as age, occupation, income, and educational level [63]. A robust brand image has been demonstrated to facilitate a congruence between consumers’ purchasing behaviors and their aspired social identities. In the context of electric vehicles, brand image serves as a heuristic for assessing quality and social acceptance. As evidenced by Han et al. (2010) [31], a luxury brand image has been found to have a substantial impact on the purchase intentions of consumers who prioritize their identity. Previous studies have examined the impact of demographic variables (such as gender, age, income, and education level) on the willingness to purchase electric vehicles. These studies include those by Hackbarth and Madlener (2016) and He et al. (2018) [29,30]. Widodo, T. and Mahadika, P. K. (2023) [64] demonstrated that price, brand image, and features of laptop computers have a significant impact on purchase intention. The impact of product attributes and brand image on purchase intention for laptop computers was found to be contingent on gender. However, the impact of occupation on the modification of purchase intentions was found to be non-significant.

In accordance with these objectives, the following hypotheses are proposed:

The following hypotheses pertain to the influence relationships:

Hypothesis 1.

Brand image positively influences purchase intention.

Hypothesis 2.

Brand perception positively affects purchase intention.

Hypothesis 3.

Brand image positively affects brand perception.

The following is the mediating role hypothesis:

Hypothesis 4.

Brand perception plays a mediating role in the process of brand image affecting purchase intention.

The following is the moderating hypothesis:

Hypothesis 5.

Gender plays a moderating role in the process of brand image affecting purchase intention, and men are more influential than women in the degree of brand image affecting purchase intention.

Hypothesis 6.

Age plays a moderating role in the process of brand image influence on purchase intention, and the higher the age, the greater the influence of brand image on purchase intention.

Hypothesis 7.

Education plays a moderating role in the process of brand image influencing purchase intention, and the higher the education, the greater the influence of brand image on purchase intention.

Hypothesis 8.

Monthly income plays a moderating role in the process of brand image influencing purchase intention, and the higher the monthly income, the greater the influence of brand image on purchase intention.

Hypothesis 9.

Occupation plays a moderating role in the influence of brand image on purchase intention.

4. Research Method and Data Testing

4.1. Research Method

4.1.1. Design of Questionnaires

In this study, a questionnaire method was used and structured surveys were statistically analyzed (regression, SEM) to determine the correlation between social identities of consumers and purchase decisions. The dimensions of social identity and purchase constructs were identified. The present study employed statistical mediation analysis and moderation analysis research methods to explore the hypothesis that social identity moderates consumers’ purchase behavior towards electric vehicles. A substantial corpus of literature has been dedicated to the topic of statistical mediation analysis and moderation analysis. These works aim to assist researchers in comprehending and implementing various methodologies for moderation and mediation analysis (Hayes, 2018, MacKinnon, 2008, VanderWeele, 2015) [65,66,67]. The questionnaire contained scales related to the social identity of electric vehicle consumers as well as the purchase of electric vehicles. These scales included a brand image scale, a brand perception scale, and a purchase intention scale. The Likert scale used for the scales ranged from 1 (strongly disagree) to 5 (strongly agree). In order to ensure the reliability and validity of the questionnaire, its questions underwent multiple revisions prior to the administration of the formal survey. These revisions were made to ensure the language used was concise and easily comprehensible. This process was carried out to protect the effectiveness and scientific integrity of the questionnaire.The content of the questionnaire is illustrated in the

Table 1. Questionnaire summary.

Brand Image	Brand Perception	Purchase Intention	Social Identity
I think the EV brand has a high level of social responsibility.	I think the EV is a reliable quality product.	I would be willing to buy the EV in the next two years.	Your gender is?
I was impressed by the ad campaign for the EV.	I am satisfied with the after sales service of the EV.	I would be more inclined to buy the EV if someone around me recommended it.	Your age is?
The product design of the EV fits my aesthetic preferences.	The range of the EV was in line with my expectations.	The price/performance ratio of the EV gives me a strong incentive to buy it.	What is your education level?
The EV excels in environmental protection and sustainability.	The price of the EV matches the value it provides.	I would prioritize the EV.	What is your monthly income range?
EV’s history and cultural heritage endeared it to me.	The technical innovations of the EV give me confidence in it.		What is your occupation?

.

4.1.2. Sample Collection

In the formal questionnaire, this paper utilizes the online questionnaire collection platform, “Questionnaire Star”, as a tool to collect data through WeChat and QQ online channels. After eliminating invalid questionnaires, 962 valid questionnaires were retained. The results of the frequency analysis of the social identity of electric vehicle consumers are shown in

Table 2. Frequency analysis result.

Item	Options	Frequency	Percentage (%)
Gender	Male	735	76.40
	Female	227	23.60
Age	18–24 years	254	26.40
	25–30 years	278	28.90
	31–36 years	261	27.13
	36–45 years	75	7.80
	Over 46 years	94	9.77
Education	Junior high or below	127	13.20
	High school	119	12.37
	Vocational school	151	15.70
	Bachelor’s degree	421	43.76
	Master’s degree or above	144	14.97
Monthly income	≤¥5000	201	20.89
	¥5001–7000	259	26.92
	¥7001–10,000	273	28.38
	¥10,001–12,000	133	13.83
	≥¥12,001	96	9.98
Occupation	Manufacturing	194	20.17
	Education	152	15.80
	Healthcare	197	20.48
	Finance	162	16.84
	Government and institutions	56	5.82
	Construction and real estate	157	16.32
	Other	44	4.57
Total		962	100.00

. The frequency analysis yielded the following results: 76.4% of the subjects identified as male, while 23.6% identified as female. The age demographic was as follows: 26.4% of subjects were aged 18–24, 28.9% were aged 25–30, 27.13% were aged 31–36, 7.8% were aged 36–45, and 9% did not specify their age. Seventy-seven percent of the sample are aged 46 and above. In terms of education level, 13.2% have a junior high school education or below, 12.37% have a high school education, 15.7% have a vocational high school education, 43.76% have a bachelor’s degree, and 14.97% have a master’s degree or above. In terms of monthly income range, 20.A survey of the Chinese workforce reveals that 89% of respondents earn 5000 yuan or less, 26.92% earn between 5001 and 7000 yuan, 28.38% earn between 7001 and 10,000 yuan, 13.83% earn between 10,001 and 12,000 yuan, and 9.98% earn 12,001 yuan or more. By occupation, the manufacturing sector accounts for 20%. The education sector accounts for 15.8%, the healthcare sector accounts for 20.48%, the financial sector accounts for 16.84%, government and public institutions account for 5.82%, and the construction and real estate sectors account for 16.32%.

4.1.3. Descriptive Analysis

For each sample, the mean value of the questions in the same dimension is calculated to represent the sample’s score in that dimension, as illustrated in

Table 3. Basic indicators.

Item	Sample Size	Mean	Std. Deviation	Kurtosis	Skewness
Brand image	962	3.054	0.971	−0.249	−0.849
Brand perception	962	3.061	1.011	−0.176	−0.893
Purchase intention	962	3.295	0.815	−0.667	−0.286

. The mean average score for brand image across all samples is 3.054, with a standard deviation of 0.971. Similarly, the mean average score for brand perception is 3.061, with a standard deviation of 1.011. Finally, the mean average score for purchase intent is 3.295, with a standard deviation of 0.815.

As illustrated in Table 3, the absolute value of the skewness of each variable is less than 3, and the absolute value of kurtosis is less than 10. This finding suggests that the key variables involved in the analysis are normally distributed, a prerequisite for subsequent analysis.

4.2. Reliability and Validity Analysis

4.2.1. Reliability Analysis

In order to be able to test whether the reliability of the questionnaire is up to standard, that is, whether the results of the questionnaire are reproducible, after the collection of the questionnaire results, the results of the questionnaire must be analyzed for reliability to prove the reliability of the questionnaire, that is, any important results must not be a one-time discovery, and the nature of the repeatable observations.

This analysis used Cronbach’s alpha coefficient to assess the reliability of the internal consistency of the questionnaire, that is, the internal consistency among the questionnaire items. When the Cronbach’s alpha coefficient of a scale exceeds 0.6, it indicates acceptable internal consistency reliability. When the coefficient exceeds 0.7, it means that the scale exhibits strong internal consistency; from the

Table 4. Cronbach reliability analysis.

Dimension	Item	Corrected Item-Total Correlation (CITC)	Deleted Item’s $\mathit{\alpha }$ Coefficient	Cronbach’s $\mathit{\alpha }$ Coefficient	Overall Reliability
Brand image	A1	0.785	0.897	0.916
	A2	0.775	0.899
	A3	0.787	0.897
	A4	0.782	0.898
	A5	0.794	0.895
Brand perception	B1	0.787	0.902	0.919
	B2	0.796	0.900		0.890
	B3	0.806	0.898
	B4	0.772	0.905
	B5	0.797	0.900
Purchase intention	C1	0.642	0.770	0.818
	C2	0.619	0.781
	C3	0.651	0.766
	C4	0.646	0.768

, the Cronbach’s alpha coefficient for each dimension exceed 0.6. The Cronbach’s alpha coefficients for the three dimensions designed in this study are 0.916, 0.919, and 0.818, respectively, while the overall reliability of the questionnaire is 0.890. All of these values are greater than 0.7, indicating excellent internal consistency. This finding suggests that the internal consistency of each dimension of the questionnaire is satisfactory. Consequently, the reliability of the survey results is considered excellent and the reliability of the questionnaire results is considered strong. This indicates that further analysis can be performed.

The term “alpha coefficient after item deletion” is used to denote the reliability coefficient subsequent to the deletion of any given item. In the event that this coefficient does not demonstrate a substantial increase, it signifies that the item should not be eliminated but instead be maintained for additional analysis. As demonstrated in the above table, the “alpha coefficient after item deletion” for all items is less than the alpha coefficient value for that dimension. Therefore, no items require deletion. The “CITC value” is employed to measure the correlation between a question and other questions in the scale. The CITC value of an item is indicative of its correlation with the overall dimension; items with CITC values greater than 0.4 exhibit a strong correlation, while those with CITC values less than 0.4 exhibit a negligible correlation. As shown in Table 4, all items exhibit CITC values greater than 0.4, suggesting a degree of correlation with the overall dimension.

4.2.2. Validity Analysis

After the reliability of the questionnaire was analyzed to meet the standard, the validity of the questionnaire was analyzed. Questionnaire validity represents the validity of the questionnaire, i.e., the extent to which the measurement instrument can measure the characteristic to be measured. This study focuses on examining the structural validity of the questionnaire, which refers to the degree of fit between the structure of the questionnaire and the intended theoretical structure. The general test questionnaire structural validity analysis method is factor analysis, there are two types of factor analysis, namely, exploratory factor analysis and validation factor analysis, they are different in the way of testing and the use of analytical tools, our consideration with the structural validity will be used in the exploratory factor analysis, the calculation formulas are as shown in Figure 4:

Prior to conducting a validity analysis employing exploratory factor analysis, it is imperative to ascertain whether the collected data are suitable for factor analysis. This procedure is carried out by employing the KMO and Bartlett’s sphericity test, as shown in

Table 5. KMO and Bartlett Test.

KMO Value		0.912
Bartlett Sphericity Test	Approx. Chi-Square	8092.845
	df	91
	p-value	0.000

. The KMO value, which is a measure of the overall quality of the data, is 0.915, which is greater than 0.6, meeting the prerequisite standard for factor analysis. This finding suggests that the data collected in this study are appropriate for factor analysis research. Furthermore, the p-value of the Bartlett sphericity test is less than 0.05, thereby confirming the reliability of the factor analysis of the collected questionnaire data.

Following the implementation of the KMO and Bartlett’s sphericity test, a subsequent examination of the particulars involved in factor extraction and the numerical values of each factor on specific indicators is imperative. From the

Table 6. Variance explained table.

Factor	Eigenvalue			Before Rotation			After Rotation
	Value	%	Cum.%	Value	%	Cum.%	Value	%	Cum.%
1	5.784	41.311	41.311	5.784	41.311	41.311	3.768	26.916	26.916
2	2.527	18.051	59.362	2.527	18.051	59.362	3.730	26.640	53.556
3	1.813	12.950	72.312	1.813	12.950	72.312	2.626	18.756	72.312
4	0.513	3.666	75.978	–	–	–	–	–	–
5	0.479	3.420	79.398	–	–	–	–	–	–
6	0.442	3.155	82.553	–	–	–	–	–	–
7	0.344	2.455	85.009	–	–	–	–	–	–
8	0.340	2.430	87.439	–	–	–	–	–	–
9	0.322	2.299	89.738	–	–	–	–	–	–
10	0.315	2.249	91.986	–	–	–	–	–	–
11	0.303	2.166	94.152	–	–	–	–	–	–
12	0.285	2.038	96.190	–	–	–	–	–	–
13	0.268	1.913	98.103	–	–	–	–	–	–
14	0.266	1.897	100.000	–	–	–	–	–	–

, factor analysis yielded a total of three factors, with the extraction criterion being an Eigenvalue greater than 1 (the extraction standard is to extract a number of factors corresponding to the number of dimensions in the questionnaire). The explained variance in these three factors after rotation is 26.916%, 26.640%, and 18.756%, respectively. Furthermore, the cumulative variance explained by these factors after rotation is 72.312%. The following text is intended to provide a comprehensive overview of the subject matter. In essence, the number of factors extracted from the scale data corresponds to the number of dimensions encompassed by the questionnaire, suggesting a certain degree of congruence between the questionnaire design structure and the data outcomes. However, the correspondence between the data results for each question and the corresponding factor remains unclear. Specifically, questions within the same dimension should correspond to the same factor. To verify the correspondence of each question to the correct factor, the maximum variance rotation method was applied, yielding the following results.

To verify the correspondence between items and factors, we employed the varimax method to rotate the factor analysis results, thereby identifying their corresponding relationships.

Table 7. Rotated factor loading coefficients.

Item	Factor Loading Coefficients			Commonality (Common Factor Variance)
	Factor 1	Factor 2	Factor 3
A1	0.110	0.842	0.169	0.750
A2	0.134	0.834	0.148	0.736
A3	0.152	0.848	0.112	0.755
A4	0.151	0.841	0.128	0.747
A5	0.126	0.846	0.166	0.759
B1	0.838	0.159	0.146	0.750
B2	0.851	0.134	0.142	0.762
B3	0.854	0.152	0.143	0.773
B4	0.838	0.122	0.125	0.732
B5	0.858	0.113	0.125	0.765
C1	0.095	0.157	0.789	0.656
C2	0.154	0.154	0.756	0.619
C3	0.182	0.159	0.773	0.656
C4	0.124	0.117	0.796	0.663

Rotation method: Varimax.

presents the factor loadings for all items (commonalities) and the correspondence between factors and items (factor loading table). Specifically, the commonality values for all research items are above 0.4, indicating that the association between items and extracted factors meets a certain standard and that factors can effectively extract information. Subsequent to the determination of commonality and the establishment of a standard, the factors are then analyzed to ascertain their capacity to extract information from the items. This analysis serves to determine whether the alignment between the factors and items aligns with theoretical expectations. The findings suggest that the observed correspondence between items and factors is consistent with our prior theoretical expectations, thereby substantiating the questionnaire’s adequate structural validity.

4.3. Correlation, Regression, and Mediation Analysis

4.3.1. Correlation Analysis

Before performing the correlation analysis, the mean of all topics belonging to the same dimension was used as the indicator of that dimension, and when performing the correlation analysis in the SPSS version 21, the indicator of each dimension was dragged into the variable box to analyze it, and the results were as follows

Table 8. Pearson correlation—standardized format.

	Brand Image	Brand Perception	Purchase Intention
Brand image	1
Brand perception	0.330 **	1
Purchase intention	0.366 **	0.348 **	1

Note ** p < 0.01.

.

The correlation coefficients for brand image, brand perception, and purchase intention are 0.366 and 0.348, respectively, with p-values less than 0.05. This finding suggests that the correlation coefficients are not equal to 0, indicating a substantial positive relationship between them. The correlation coefficient between brand image and brand perception is 0.330, and the p-value is less than 0.05, indicating that the correlation coefficient is not equal to 0, and the two variables have a significant positive correlation. In summary, the correlations between variables are significant, meeting the prerequisites for correlation regression analysis. Therefore, further regression analysis can be conducted.

4.3.2. Regression Analysis

As illustrated in

Table 9. Linear regression analysis results ($n=962$).

	Unstandardized Coefficients		Standardized	t	p
	B	Std. Error	Beta
Constant	2.012	0.102	–	19.787	0.000 **
Brand image	0.343	0.032	0.330	10.821	0.000 **
$R^2$	0.109
Adjusted $R^2$	0.108
F	$F(1,960)=117.083$, $p=0.000$
D-W value	1.927

Note: Dependent variable = brand perception. ** p < 0.01.

, when brand image is designated as the independent variable and brand perception is the dependent variable in a linear regression analysis, the model formula is as follows. The brand perception equation is derived as follows: Brand Perception = 2.012 + 0.343 × Brand Image. The model’s R-squared value is 0.109, indicating that brand image accounts for 10.9% of the variation in brand perception. The results of the F-test on the model indicated a statistically significant relationship between brand image and brand perception, as evidenced by the model’s passing of the F-test (F = 117.083, p = 0.000 < 0.05). A subsequent examination of the data indicates that the regression coefficient for brand image is 0.343 (t = 10.821, p = 0.000 < 0.01), suggesting that brand image exerts a substantial positive influence on brand perception. In summary, it can be concluded that brand image exerts a significant positive influence on brand perception.

As illustrated in

Table 10. Parameter estimates ($n=962$).

	Unstandardized Coefficients		Standardized	t	p	Collinearity Statistics
	B	Std. Error	Beta			VIF	Tolerance
Constant	1.944	0.093	–	20.972	0.000 **	–	–
Brand image	0.236	0.026	0.282	9.156	0.000 **	1.122	0.891
Brand perception	0.206	0.025	0.255	8.300	0.000 **	1.122	0.891
$R^2$	0.192
Adjusted $R^2$	0.190
F	$F(2,959)=113.782$, $p=0.000$
D-W value	1.600

Note: Dependent variable = Purchase intention. ** p < 0.01.

, a linear regression analysis was conducted with brand image and brand perception as the independent variables and purchase intention as the dependent variable. The equation for purchase intention is 1.944 + 0.236 × Brand Image + 0.206 × Brand Perception. The model’s R-squared value is 0.192, meaning brand image and brand perception explain 19.2% of the variation in purchase intention. The model also passed the F-test (F = 113.782, p < 0.05), which indicates that at least one of the independent variables has a significant influence on the dependent variable.

The regression coefficient for brand image is 0.236 (t = 9.156, p = 0.000 < 0.01), which indicates that brand image significantly and positively influences purchase intention. Similarly, the regression coefficient for brand perception is 0.206 (t = 8.300, p = 0.000 < 0.01), suggesting that brand perception also significantly influences purchase intention.

4.3.3. Mediation Analysis

In the study of the role of mediation, there are a variety of practices, of which two are particularly common. The first is the causal step-by-step regression test, which involves the use of stratified regression for research. The second is the product coefficient method test, which can be further subdivided into the Sobel test and the bootstrap sampling method test. The former approach is relatively straightforward and easily comprehensible; consequently, it is extensively utilized. However, its efficacy in testing is comparatively lower. The former method tests the outcomes of the test, while the bootstrap method tests the outcomes of the test that may be insignificant. Consequently, the prevailing practice that is currently more suitable is the utilization of the second method, the product coefficient test, and the implementation of the bootstrap sampling method for the mediation test.

The basic theoretical mathematical model for the test of mediation is as follows Figure 5.

As illustrated in Figure 5, a represents the first half of the mediation path, b represents the second half of the mediation path, c represents the total effect, c’ represents the direct effect, and the product term of the regression coefficient a and the regression coefficient b (a*b) is known as the indirect effect. If the value of a*b shows significance then it indicates that there is a mediating effect, and conversely if it is not significant, then it indicates that there is no mediating effect. Testing the significance of a*b is used to determine whether there is a mediating effect or not, this practice is known as the product coefficient test. This method requires the use of bootstrap sampling method, and the test criterion is whether the 95% confidence interval of the regression coefficient of a*b includes the number 0; if it is said that the 95% confidence interval does not include the number 0, it means that there is a mediating role and if it is said that the 95% confidence interval includes the number 0, it means that there is no intermediary role.

As illustrated in

Table 11. Mediation effects model test—stratified regression.

	Purchase Intention	Brand Perception	Purchase Intention
Constant	2.358 ** (29.170)	2.012 ** (19.787)	1.944 ** (20.972)
Brand image	0.307 ** (12.174)	0.343 ** (10.821)	0.236 ** (9.156)
Brand perception	–	–	0.206 ** (8.300)
Sample size	962	962	962
$R^2$	0.134	0.109	0.192
Adjusted $R^2$	0.133	0.108	0.190
F value	$F(1,960)=148.195$ $p=0.000$	$F(1,960)=117.083$ $p=0.000$	$F(2,959)=113.782$ $p=0.000$

Note: ** p < 0.01. Values in parentheses are t-statistics.

, in Model 1, brand image has a significant positive effect on purchase intention ($\beta$ = 0.307, p < 0.05). This indicates that the total effect (c) in the mediating mathematical model is significant; that is, it is significantly different from 0 (not equal to 0).

In Model 2, brand image significantly and positively affects brand perception ($\beta$ = 0.343, p < 0.05). This indicates that the value of the coefficient a in the mediating mathematical model is significant, meaning it is distinctly different from 0 (not equal to 0).

Model 3 incorporates a mediating variable based on Model 1. Brand image significantly and positively influences purchase intention ($\beta$ = 0.236, p < 0.05), indicating that the direct effect (c’) in the mediating mathematical model is significant; that is, it differs significantly from zero. Brand perception significantly and positively influences purchase intention ($\beta$ = 0.206, p < 0.05), indicating that b in the mediating mathematical model is significant.

The stratified regression table showed that the significance of the mediating paths, a and b, met the criteria through stepwise regression testing. The following table shows the results of testing the mediating effect using the product method. The 95% confidence interval of a*b was calculated using the bootstrap method to determine the significance of the product term, thus determining whether a mediating effect existed.

As illustrated in

Table 12. Summary of intermediation test results.

Item	c Total Effect	a	b	$\mathit{a}\ast \mathit{b}$ Value of Intermediary Effect	$\mathit{a}\ast \mathit{b}$ (95% BootCI)	${\mathit{c}}^{\prime }$ Direct Effect	Test Conclusion
Brand image ⇒ Brand perception ⇒ Purchase intention	0.307 **	0.343 **	0.206 **	0.071	0.062∼0.108	0.236 **	part of an intermediary

Note: ** p < 0.01, bootstrap style = percentile bootstrap method.

, the mediating effect value in the path “brand image → brand perception → purchase intention” is 0.071, with a bootstrap confidence interval of 0.062 to 0.108. Since the interval does not include 0, the mediating effect is significant. Additionally, the direct effect is significant, indicating that the mediator is a partial mediator.

Overall, the stratified regression table and the bootstrap results consistently show that the mediating effect aligns with the expected hypothesis, indicating that the mediating variable transmits influence from the independent to the dependent variable.

4.4. Moderating Effects

The moderating effect is the study of how a moderating variable affects the influence of an independent variable on a dependent variable. For instance, when a person’s self-confidence is high, their frequency of social interaction will affect how positively others evaluate them.

The PROCESS plugin in SPSS version 21 primarily uses two criteria to analyze moderation effects: first, the change in the F-value when the interaction term is added to the regression equation, and second, the interaction term’s significance level. This analysis uses the second method. If the interaction term’s significance level in the regression equation is less than 0.05, we can conclude that a moderating effect exists. This means that the moderator variable interferes with the relationship between the independent and dependent variables.

There are two ways to examine the moderating effect. First, examine the significance of the change in the F-value when comparing Models 2 and 3. The second method is to examine the significance of the interaction term in Model 3. In this study, the moderating effect was analyzed using the second method.

As illustrated in

Table 13. Moderated effects analysis of gender.

	Model 1	Model 2	Model 3
Constant	3.295 ** (134.651)	3.295 ** (141.180)	3.283 ** (146.977)
Brand image	0.307 ** (12.174)	0.295 ** (12.233)	0.260 ** (11.147)
Gender (1 = male, 2 = female)	–	−0.540 ** (−9.816)	−0.581 ** (−11.018)
Brand image × Gender	–	–	−0.583 ** (−9.639)
Sample size	962	962	962
$R^2$	0.134	0.213	0.282
Adjusted $R^2$	0.133	0.211	0.280
F value	$F(1,960)=148.195$ $p=0.000$	$F(2,959)=129.633$ $p=0.000$	$F(3,958)=125.673$ $p=0.000$
$\Delta R^2$	0.134	0.079	0.070
$\Delta F$ value	–	$F(1,959)=96.351$ $p=0.000$	$F(1,958)=92.906$ $p=0.000$

Note: Dependent variable = Purchase intention. ** p < 0.01. Values in parentheses are t-statistics.

, the moderating effect is divided into three models. Model 1 includes the independent variable, brand image. Model 2 adds the moderating variable, gender, to Model 1. Model 3 adds the interaction term, which is the product of the independent and moderating variables, to Model 2.

Model 1 examines the effect of the independent variable, brand image, on the dependent variable, purchase intention, while controlling for the moderating variable, gender. Brand image is a significant predictor of purchase intention (t = 12.174, p = 0.000 < 0.05). This indicates that brand image significantly affects purchase intention.

The interaction term between brand image and gender is significant (t = −9.639, p = 0.000 < 0.05). This indicates that the degree to which brand image influences purchase intention varies significantly depending on the moderator variable (gender). Specifically, brand image has the greatest positive influence on purchase intention when gender is male. This relationship can be further elucidated through the simple slope chart(see Figure 6) presented in the subsequent section.

As illustrated in

Table 14. Moderated effects analysis results of age.

	Model 1	Model 2	Model 3
Constant	3.295 ** (134.651)	3.295 ** (135.942)	3.292 ** (136.401)
Brand image	0.307 ** (12.174)	0.302 ** (12.084)	0.297 ** (11.904)
Age	–	0.087 ** (4.415)	0.085 ** (4.322)
Brand image × Age	–	–	0.066 ** (3.357)
Sample size	962	962	962
$R^2$	0.134	0.151	0.161
Adjusted $R^2$	0.133	0.149	0.158
F value	$F(1,960)=148.195$ $p=0.000$	$F(2,959)=85.272$ $p=0.000$	$F(3,958)=61.214$ $p=0.000$
$\Delta R^2$	0.134	0.017	0.010
$\Delta F$ value	$F(1,960)=148.195$ $p=0.000$	$F(1,959)=19.495$ $p=0.000$	$F(1,958)=11.270$ $p=0.001$

Note: Dependent variable = Purchase Intention. ** p < 0.01. Values in parentheses are t-statistics.

, the moderating effect is divided into three models. Model 1 includes the independent variable, brand image. Model 2 adds the moderating variable, age, to Model 1. Model 3 adds the interaction term, the product of the independent and moderating variables, to Model 2.

The objective of Model 1 is to investigate the influence of the independent variable (brand image) on the dependent variable (purchase intention) while controlling for the moderator variable (age). As shown in the above table, brand image exhibits significant influence (t = 12.174, p = 0.000 < 0.05). This indicates that brand image significantly impacts purchase intention.

The interaction term between brand image and age is also significant (t = 3.357, p = 0.001 < 0.05). This suggests that when brand image influences purchase intention, the moderator variable (age) results in significant differences in the magnitude of influence at different levels; that is, the greater the age, the greater the positive influence of brand image on purchase intention. The subsequent simple slope chart (see Figure 7) provides a visual representation of this relationship, demonstrating the varying degrees of influence of brand image on purchase intention across different age groups.

As can be seen from

Table 15. Moderated effects analysis of education.

	Model 1	Model 2	Model 3
Constant	3.295 ** (134.651)	3.295 ** (135.558)	3.243 ** (128.762)
Brand image	0.307 ** (12.174)	0.337 ** (12.813)	0.309 ** (11.859)
Education	–	−0.076 ** (−3.740)	−0.042 * (−2.038)
Brand image × Education	–	–	0.143 ** (6.405)
Sample size	962	962	962
$R^2$	0.134	0.146	0.181
Adjusted $R^2$	0.133	0.144	0.179
F value	$F(1,960)=148.195$ $p=0.000$	$F(2,959)=82.093$ $p=0.000$	$F(3,958)=70.687$ $p=0.000$
$\Delta R^2$	0.134	0.012	0.035
$\Delta F$ value	$F(1,960)=148.195$ $p=0.000$	$F(1,959)=13.985$ $p=0.000$	$F(1,958)=41.025$ $p=0.000$

Note: Dependent variable = Purchase intention. * p < 0.05, ** p < 0.01. Values in parentheses are t-statistics.

, the moderating effect is divided into three models. Model 1 includes the independent variable, brand image. Model 2 adds the moderating variable (educational attainment) to Model 1. Model 3 adds the interaction term (the product of the independent and moderating variables) to Model 2.

Model 1 investigates the influence of the independent variable (brand image) on the dependent variable (purchase intention) without considering the moderator variable (educational attainment). As shown in the table, the independent variable exhibits significant results (t = 12.174, p = 0.000 < 0.05). This indicates that brand image significantly influences purchase intention.

The interaction term between brand image and educational attainment is also significant (t = 6.405, p = 0.000 < 0.05). This indicates that, when brand image influences purchase intention, the moderator variable (education level) significantly differs in magnitude of influence at different levels; the higher the education level, the stronger the positive influence of brand perception on purchase intention. which can be viewed in the next simple slope chart (see Figure 8).

As illustrated in

Table 16. Moderating effect analysis of monthly income range.

Variable	Model 1	Model 2	Model 3
Constant	3.295 ** (134.651)	3.295 ** (134.875)	3.258 ** (132.059)
Brand image	0.307 ** (12.174)	0.294 ** (11.322)	0.292 ** (11.480)
Monthly income range	–	0.042 * (2.050)	0.026 (1.298)
Brand image × Monthly income range	–	–	0.127 ** (6.295)
N	962	962	962
$R^2$	0.134	0.138	0.172
Adj. $R^2$	0.133	0.136	0.169
F	$F(1,960)=148.195$ $p=0.000$	$F(2,959)=76.447$ $p=0.000$	$F(3,958)=66.228$ $p=0.000$
$\Delta R^2$	–	0.004	0.034
$\Delta F$	–	$F(1,959)=4.204$ $p=0.041$	$F(1,958)=39.631$ $p=0.000$

Note: Dependent Variable = Purchase Intention. * p < 0.05, ** p < 0.01. Values in parentheses are t-statistics.

, the moderating effect is divided into three models. Model 1 includes the independent variable, brand image. Model 2 adds the moderating variable, monthly income range, to Model 1. Model 3 adds the interaction term, the product of the independent and moderating variables, to Model 2.

Model 1 investigates the influence of the independent variable (brand image) on the dependent variable (purchase intention) without considering the moderator variable (monthly income range). The independent variable exhibits significant results (t = 12.174, p = 0.000 < 0.05). This indicates that brand image significantly influences purchase intention.

The interaction term between brand image and monthly income range is also significant (t = 6.295, p = 0.000 < 0.05). This indicates that, when brand image influences purchase intention, the moderator variable has a significant difference in influence magnitude at different levels. As illustrated in the subsequent simple slope chart (see Figure 9).

As shown in

Table 17. Moderating effects of occupation on the relationship between brand image and purchase intention.

	Model 1	Model 2	Model 3
Constant	(134.651)	(61.748)	(62.583)
Brand image	0.307 ** (12.174)	0.294 ** (11.506)	0.122 * (2.118)
Occupation: manufacturing (ref.)	–	–	–
Occupation: education	–	0.047 (0.575)	−0.022 (−0.261)
Occupation: healthcare	–	−0.053 (−0.694)	−0.042 (−0.565)
Occupation: finance	–	−0.125 (−1.549)	−0.111 (−1.406)
Occupation: government	–	0.052 (0.450)	0.014 (0.118)
Occupation: construction & Real Estate	–	−0.225 ** (−2.773)	−0.263 ** (−3.236)
Occupation: other	–	0.041 (0.326)	0.048 (0.386)
Brand image × education	–	–	0.449 ** (5.318)
Brand image × healthcare	–	–	0.180 * (2.351)
Brand image × finance	–	–	0.189 * (2.209)
Brand image × government	–	–	0.366 ** (3.142)
Brand image × construction & Real Estate	–	–	−0.041 (−0.459)
Brand image × other	–	–	0.127 (1.023)
Sample size	962	962	962
$R^2$	0.134	0.147	0.184
Adjusted $R^2$	0.133	0.141	0.172
F value	$F(1,960)=148.195$, $p=0.000$	$F(7,954)=23.463$, $p=0.000$	$F(13,948)=16.398$, $p=0.000$
$\Delta R^2$	0.134	0.013	0.037
$\Delta F$ value	$F(1,960)=148.195$, $p=0.000$	$F(6,954)=2.450$, $p=0.023$	$F(6,948)=7.105$, $p=0.000$

Note: Dependent variable = Purchase Intention. * p < 0.05, ** p < 0.01. The t-value is shown in parentheses.

, the moderating effect is divided into three models. Model 1 includes the independent variable, brand image. Model 2 adds the moderating variable, occupation, to Model 1. Model 3 adds the interaction term, which is the product of the independent and moderating variables, to Model 2.

The objective of Model 1 is to investigate the influence of the independent variable (brand image) on the dependent variable (purchase intention) without considering the moderator variable (occupation).The independent variable exhibits statistical significance (t = 10.865, p = 0.000 < 0.05). This indicates that brand image significantly influences purchase intention.

The interaction terms between brand image and occupational virtualization have significant effects on the education, healthcare, and financial industries, as well as on the civil service and public institutions. Additionally, Model 1 indicates that brand image influences purchase intent. Therefore, when brand image influences purchase intent, the moderating variables have inconsistent influence magnitudes across different industries and institutions. Furthermore, the influence of brand image on purchase intent becomes stronger when the occupation is in the education, healthcare, financial, civil service, or public institutions industries. The subsequent simple slope diagram can further examine this (see Figure 10, Figure 11, Figure 12 and Figure 13).

5. Conclusions and Discussion

5.1. Discussion

The findings indicate that the research hypotheses have been validated. Gender exerts a moderating influence on the relationship between brand image and purchase intention, with males demonstrating a stronger association between brand image and purchase intention compared to females. This finding aligns with the conclusions of previous studies, including those by Graham-Rowe et al. (2012) and Plötz et al. (2014) [68,69], which indicated a higher propensity among males to acquire electric vehicles. According to the findings of Sovacool, B. K., et al. (2018) [70], gender was a consistent and significant influencing factor related to car use. The impact of age on the relationship between brand image and purchase intention is moderated by various factors. Specifically, the influence of brand image on purchase intention is found to be more significant in older age groups. This finding is at odds with the conclusions of several studies (Chen et al., 2020; Jia and Chen, 2021; Tomasi et al., 2021; Yang et al., 2023; Ziegler, 2012) [71,72,73,74,75]. These studies have indicated that older individuals demonstrate a lower propensity to utilize electric vehicles, while younger demographics exhibit a heightened level of interest in doing so. The monthly income of consumers has been demonstrated to exert a moderating influence on the relationship between brand image and purchase intention. Specifically, it has been observed that an increase in monthly income corresponds to a corresponding increase in the degree to which brand image influences purchase intention. This finding aligns with the conclusions of Ouyang, D., Ou, X., Zhang, Q., and Dong, C. (2020) [76], which demonstrated a positive correlation between higher income and the likelihood of acquiring an electric vehicle, as well as between certain license plate preferences and the same outcome.

Educational qualifications have been shown to moderate the relationship between brand image and purchase intention, with higher levels of educational attainment correlating with a more significant impact of brand image on purchase intention. As demonstrated in the studies by Bennett et al., He and Zhan, Jenn et al., and Kim et al. (2018) [77,78,79,80], well-educated professional consumers exhibit a marked preference for electric vehicles. In Sweden, research has demonstrated that a “high level of education” is a salient characteristic among early adopters of EVs (Vassileva and Campillo, 2017) [81]. In Norway, a correlation has been observed between EV adoption and higher levels of education among drivers. This phenomenon is further supported by the findings that EV owners report “high levels of motivation” concerning environmental concerns, as well as cost considerations (McKinsey Company, 2014) [82]. However, Brand and Preston (2010) have raised questions regarding the correlation between education and low-carbon transportation, positing that individuals engaged in higher education, including college attendance or other forms of full-time education, exhibit substantially elevated transportation-related emissions in comparison to their non-educated counterparts [83]. Contrary to prior studies, the present analysis demonstrates that occupation does not function as a moderating factor in brand image’s influence on purchase intentions. This outcome stands in opposition to the hypothesis proposed by Sovacool et al. (2012), who suggested that individuals engaged in industrial occupations would underestimate the significance of mitigating climate change and reducing environmental degradation [84]. A Swedish study further noted that individuals in the traditional automotive industry tend to have a strong preference for regular cars and resist electric cars due to reduced after-sales revenues (Nykvist and Nilsson, 2015) [85].

5.2. Conclusions

This study is based on the theory of social constructionism and the model of the consumer decision-making process. It systematically explores the causal relationships among electric vehicle brand image, brand perception, and purchase intention. Brand image is used as the independent variable and brand perception as the dependent variable in a linear regression analysis. The results of the regression analysis indicate that brand image significantly and positively influences brand perception. Similarly, the results indicate that brand perception significantly and positively influences purchase intention. The regression coefficients for brand cognition show that brand cognition significantly and positively influences purchase intention. These results reveal the mediating effect of brand cognition and the moderating effect of social identity. Specifically, the value of the mediating effect of the path “brand image → brand cognition → purchase intention” indicates that the effect is statistically significant. Additionally, the direct effect is significant, indicating that the mediating effect plays a partial role. These results are consistent with our hypothetical model, indicating that the mediating variable acts as a transmission mechanism through which the independent variable influences the dependent variable.

The moderating effect refers to the presence of moderating variables that interfere with the process by which the independent variable of social identity influences the dependent variable of brand image. The interaction term between brand image and gender is statistically significant. This indicates that brand image’s influence on purchase intention varies significantly by gender, with a stronger positive effect on male consumers. The interaction term between brand image and age is also significant. This suggests that brand image’s influence on purchase intention varies significantly across age groups, with a stronger positive effect in older groups. Similar results were observed in the interaction term between brand image and educational attainment. This suggests that brand image has a stronger positive influence on purchase intention in higher educational levels. Specifically, the higher the educational attainment, the greater the positive influence of brand recognition on purchase intention. The interaction term between brand image and monthly income range is also statistically significant. This suggests that, when brand image influences purchase intention, the moderating variable (monthly income range) has significantly different effects at different levels. Conversely, the interaction term between brand image and occupation was statistically significant, indicating that the influence of brand image on purchase intention varies by gender and is stronger in the education, healthcare, finance, civil service, and public institution sectors.

5.3. Research Limitations and Prospects for Future Works

5.3.1. Research Limitations

This study examines, from a social constructivism perspective, how brand image and brand perception positively influence the purchase intention of electric vehicle consumers, especially the moderating effect of the social identity of electric vehicle consumers on brand image, which in turn influences consumers’ purchase intentions. While the survey addressed the influence of demographic factors, including gender, age, income, education, and occupation, on brand image, and while analyses based on demographic moderators are statistically reliable, they may overlook the interaction between intersecting psychological factors (individualism vs.collectivism, openness to innovation) and socio-technical perceptions of EVs. Consequently, the study’s depth and breadth are limited.

Furthermore, although this paper analyzes the reliability and validity of the data collected from the questionnaire, it is important to be wary of the potential impact of social desirability bias on the results. It is plausible that respondents may exaggerate positive evaluations of the sustainable or innovative attributes of EV brands by appealing to dominant societal values (environmentalism, cult of technology) (Fisher, 1993) [86]. For instance, consumers may assert a “priority for the environment” to showcase their moral superiority, yet their actual purchasing decisions are still constrained by factors such as cost and practicality. This phenomenon may result in an overestimation of the relationship between brand perceptions and purchase intentions, thereby compromising the external validity of the findings. Women, older adults, and offline populations are underrepresented, which may obscure gender, generational, or accessibility-related perspectives. The sample does not reflect urban-rural differences, regional differences (coastal versus inland provinces), or differences in infrastructure and policy implementation across China. Furthermore, the exclusion of offline populations may overlook socio-economic or technological barriers faced by populations with limited access to the Internet.

The sample is disproportionately comprised of younger individuals residing in urban areas who are proficient in internet usage. However, this method is not without its limitations. It may underestimate the demand for electric vehicles among middle-aged and older groups, systematically weaken the moderating effect of infrastructure accessibility variables, and naturally filter out low-income groups.The study employed a one-time questionnaire, which is incapable of capturing the dynamic evolution of brand image or the causal mechanism of long-term consumer behavior. The model’s limitations include the exclusion of policy environment factors, such as purchase subsidies and carbon tax policies, infrastructure elements like charging pile coverage, and cultural differences, particularly the contrasting values between East and West [87,88,89]. These factors may prove to be key moderating elements that were not incorporated into the model.

5.3.2. Future Works Prospects

This study preliminarily verified the moderating effect of single social identity variables, such as gender, age, and education, on brand image. However, the study did not explore the interaction effect of multi-dimensional identity. In future research, latent category analysis (LCA) or machine-learning models (random forests, decision trees) can be employed to identify the differentiated effects of different combinations of social identities on brand image perceptions [90,91]. These models can also be used to construct a “social identity portrait” of segmented populations, in order to guide precise marketing strategies. To mitigate response bias arising from respondents aligning with mainstream social values (environmental protection, technology preference), a contextual simulation or discrete choice experiment (DCE) was conducted to observe consumers’ genuine preferences in trade-offs (price vs. environmental protection) [92]. The present sample is focused on young male Internet users in China, with a priority placed on the recruitment of balanced subgroups (age, gender, rural/urban residents) to reflect the diversity of China. The incorporation of offline data collection methodologies, such as community surveys, is recommended to mitigate numerical bias. Conduct parallel studies in underrepresented Chinese regions (western provinces) and other global markets to identify context-specific versus universal factors in EV symbolism.

This study used a one-time cross-sectional survey that could not capture the causal mechanisms of dynamic brand image evolution or long-term consumer behavior. Quantitative data are supplemented with periodic qualitative interviews to deepen understanding of personal identity evolution and brand relationship narratives. Growth curve or time series analysis is used to map the trajectory of change, identify key inflection points (policy shifts, technological advances), and assess the two-way interaction between identity and brand. The subsequent phase of the study will entail the utilization of longitudinal panel data or digital trace tracking methodologies, such as social media comments and e-commerce platform browsing records, to capture the dynamic evolution of brand image in response to policy adjustments and technological iterations. This approach will elucidate the causal chain between short-term attitudes and long-term behaviors [93,94,95,96]. The utilization of hierarchical modeling facilitates the integration of individual consumer data into regional or national frameworks, thereby enabling the analysis of subsidy tiers and infrastructure investment levels. Collaborations with industry and government agencies can facilitate the acquisition of policy timelines, infrastructure maps, and subsidy registries, thereby enhancing the empirical rigor of the research. It is imperative to illustrate how external factors, such as reductions in subsidies, influence the relationship between consumers and brands, thereby providing stakeholders with actionable insights.