Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 1453-1470 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 24 February 2025; Revised: 19 April 2025; Accepted: 24 April 2025; Published: 14 May 2025 * Correspondence: d974010008@gmail.com The influence of green innovative products on brand love: The intermediary effect of perceived uniqueness and brand acceptance and the moderating effect of green social responsibility——A case study of new energy vehicles Yu Zhang1, Sze Ting Chen2* 1,2Chinese International College, Dhurakij Pundit University, Bangkok, Thailand; zhangzhang_zzz1234@163.com (Y.Z.) d974010008@gmail.com (S.T.C.). Abstract: This study investigates the impact of innovative green products on brand love, focusing on the mediating roles of perceived uniqueness and brand acceptance, and the moderating effect of green social responsibility. Using stratified random sampling, 1,667 questionnaires were distributed among consumers familiar with green automotive innovations. Structural Equation Modeling (SEM) was employed to analyze the interrelationships among variables. Findings reveal that green innovative products positively influence brand love, with perceived uniqueness and brand acceptance significantly mediating this relationship. Additionally, green social responsibility moderates the strength of these mediating effects, either enhancing or weakening the emotional bond depending on consumer alignment with environmental values. The study provides empirical evidence that innovation and consumers’ perception of uniqueness and acceptance shape emotional attachment to green brands. The novelty of this research lies in its integrated model that combines dual mediators and a moderator within the context of new energy vehicle brands, offering new insights into brand loyalty development in sustainability-driven markets. The results suggest that brands should emphasize product uniqueness and foster consumer acceptance to enhance emotional connection while aligning green social responsibility initiatives with consumer values to reinforce brand love. These findings offer practical and theoretical guidance for marketers and enterprises aiming to optimize green innovation strategies, build stronger consumer-brand relationships, and enhance long-term brand loyalty in the context of sustainable development. Keywords: Brand acceptance, Brand love, Green innovative products, Green social responsibility, Perceived Uniqueness. 1. Introduction Growing threats such as climate change, resource depletion, and ecosystem degradation have made green innovation a strategic priority for businesses and governments [1]. New energy vehicles (NEVs) exemplify this innovation in the automotive sector by advancing technology and enhancing consumer sustainability awareness [2]. NEVs reduce emissions and improve air quality, offering a viable solution for sustainable transportation [3]. As environmental consciousness intensifies, consumers increasingly factor green attributes and corporate social responsibility into their purchasing decisions [4]. Consequently, NEV brands must employ effective communication and sustainability marketing to differentiate themselves [5]. Brand preference and brand love are an emotional bond between consumers and brands that significantly drive loyalty and word-of-mouth advocacy [6]. Brand love has thus received growing academic attention for its ability to foster long-term loyalty and competitive advantage [7]. Within the NEV market, perceived uniqueness, brand acceptance, and green social responsibility emerge as key determinants of brand love [8]. 1454 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Perceived uniqueness is the extent to which consumers view a brand as distinct, which is essential for brand differentiation. In the context of new energy vehicles (NEVs), this uniqueness is driven by technological innovation, design excellence, and sustainability efforts. Brand acceptance reflects the degree to which consumers resonate with a brand, shaped by its values, environmental commitments, and overall credibility. These factors significantly influence brand loyalty and foster deeper emotional connections with consumers [9]. Green social responsibility (GSR) encompasses corporate practices prioritizing environmental protection and sustainability. When effectively implemented, GSR enhances brand image and strengthens emotional ties between consumers and the brand, particularly in the NEV sector [10]. Green innovation spans the entire product lifecycle, from design to end-use, and includes sustainable practices across production, supply chains, and operations [11]. Companies that adopt green innovation strategies gain a competitive edge and meet the growing consumer demand for eco-friendly products [12]. Amid increasing economic and environmental pressures, green innovation has emerged as a critical strategy for corporate growth. New energy vehicles (NEVs), as a prime example, not only provide environmental benefits but also shape consumer perceptions and enhance market competitiveness [13]. With rising environmental awareness, consumers now prioritize sustainability and corporate social responsibility (CSR), making brand love a central focus in marketing research [14]. However, the pathways through which green innovation influences brand love remain underexplored. This study focuses on three key factors: perceived uniqueness, brand acceptance, and green social responsibility, which are believed to influence brand love significantly. Perceived uniqueness enhances brand appeal by highlighting distinctive and innovative features, particularly in NEVs, where personalization and sustainability are crucial to consumer preferences [15]. Brand acceptance, which reflects alignment with brand values, fosters emotional attachment and loyalty [16]. Green social responsibility bolsters brand image and consumer trust, positively influencing purchase intentions [17]. This study aims to examine how green innovation products influence consumer brand love, using new energy vehicles (NEVs) as the research context. It proposes an integrated model that incorporates perceived uniqueness and brand acceptance as mediating variables, while green social responsibility serves as a moderating factor. Specifically, the study seeks to assess the direct impact of green innovation on brand love; investigate the mediating roles of perceived uniqueness and brand acceptance in this relationship; evaluate the moderating effect of green social responsibility on the connections among green innovation, perceived uniqueness, brand acceptance, and brand love; and finally, to construct and empirically validate a comprehensive model that clarifies the interrelationships among these key variables. 2. Literature Review 2.1. Brand Equity Theory Brand equity refers to the value a brand holds in the minds of consumers, shaped by their perceptions, attitudes, and behaviours [18]. A foundational model comprising brand loyalty, brand awareness, brand associations, and perceived quality, emphasizing the importance of emotional connections and brand recognition, considered to be the Customer-Based Brand Equity (CBBE) model, which focuses on consumers' cognitive responses, trust, and the formation of positive brand associations [19]. Over time, brand equity has evolved into a strategic asset, significantly influencing pricing power, market share, and customer loyalty Netemeyer, et al. [20]. Yoo and Donthu [21] proposed multidimensional measures of brand equity and underscored its financial implications. With the rise of digital transformation, consumer interactions, online reviews, and social media engagement have become critical in shaping brand value [22]. Recent research also integrates emotional dimensions, such as Brand Love, highlighting how strong emotional bonds drive consumer loyalty and purchase 1455 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate intentions [23]. Today, brand equity is understood as a multidimensional construct encompassing cognitive, emotional, and strategic elements, serving as a vital driver of long-term business success. 2.2. Corporate Social Responsibility (CSR) Theory Corporate Social Responsibility (CSR) theory was first formally introduced by Bowen [24] who argued that businesses should not solely focus on profits but also take responsibility for their social impacts, including environmental protection and community well-being. Carroll [25] refined this concept by proposing the CSR Pyramid, outlining four dimensions: economic, legal, ethical, and philanthropic responsibilities, offering a structured framework for CSR practices. During the 1990s, CSR evolved into a strategic tool. Waddock and Graves [26] identified a positive relationship between CSR initiatives and financial performance, positioning CSR as a moral obligation and a strategic investment. Further advancement can be made by introducing "Creating Shared Value" (CSV), emphasizing that CSR should align social progress with economic success. Matten and Moon [27] distinguished between explicit and implicit CSR, highlighting the importance of cultural and institutional contexts in shaping CSR practices. In recent years, the role of CSR, particularly Green Social Responsibility (GSR), has gained prominence for its impact on brand image, consumer loyalty, and employee engagement [28]. Technological advancements, such as blockchain, have enhanced CSR transparency and consumer trust [29] while CSR-driven innovation has supported sustainability efforts and market expansion [30]. Despite its benefits, including strengthened brand trust, improved employee morale, and competitive advantage, CSR implementation can entail significant costs, particularly for small and medium-sized enterprises (SMEs) [31]. Risks such as greenwashing can also undermine credibility and consumer trust [32]. In this study, CSR, especially GSR, forms a key theoretical foundation linking green innovation products to brand love, perceived uniqueness, and brand acceptance. Companies that actively fulfil GSR obligations tend to foster stronger emotional bonds with consumers, enhance brand differentiation, and improve consumer trust and acceptance, positioning CSR as a critical moderating factor in brand- building strategies [33]. 2.3. Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM), proposed by Davis [34] explains users’ acceptance of technology based on two key factors: Perceived Ease of Use (PEU) and Perceived Usefulness (PU), the perceived simplicity of using a system and the anticipated benefits it provides. These perceptions shape users' behavioural intentions and actual usage behaviours. TAM has been widely applied across various domains, including information systems and consumer behaviour. It has evolved through TAM2 [35] which incorporated Social Influence and Cognitive Processes, and TAM3 [36] which introduced individual and contextual factors, offering a more comprehensive understanding of technology acceptance. TAM has increasingly been used to study consumer adoption of green technologies, such as electric vehicles and buildings. Research by [37] demonstrated that PU (e.g., environmental benefits) and PEU (e.g., ease of use) significantly influence consumer acceptance of these innovations. Although effective, TAM has been criticized for oversimplifying complex user behaviours. Integrating TAM with broader theoretical frameworks can enhance its explanatory power. This study employs TAM to explain consumer acceptance of green innovation products. Green Corporate Social Responsibility (GCSR) is proposed as a moderating variable, enhancing PU and PEU by strengthening brand trust and consumers' environmental identity and encouraging the adoption of electric vehicles. 1456 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate 2.4. Brand Love 2.4.1. Consumer Brand Love’s Impact on Corporate Behavioral Characteristics Consumer Brand Love refers to the deep emotional attachment that consumers form with a brand, which leads to strong loyalty, affection, and advocacy [38]. Unlike simple brand satisfaction, Brand Love fosters enduring emotional connections that result in repeat purchases, positive word-of-mouth, and a willingness to recommend the brand to others [39, 40]. Consumers experiencing Brand Love exhibit resilience, continuing to support the brand even in times of crisis, thereby mitigating negative reputational impacts and reducing churn [41]. Additionally, they actively contribute to brand community building, strengthening loyalty and providing valuable insights through participation in brand-related discussions and events [42]. Brand Love extends beyond purchasing behaviour. Consumers are willing to pay premium prices, defend the brand from criticism, and innovate through feedback and co-creation [43]. Furthermore, it enhances the brand’s perceived uniqueness, helping it stand out in the market and command higher prices [44]. In conclusion, Brand Love is pivotal in influencing consumer loyalty, advocacy, resilience, innovation, and the brand's competitive positioning. It enables brands to maintain a solid customer base, foster meaningful brand communication, and drive market success. 2.4.2. The Impact of Consumer Brand Love on Enterprises Brand Love significantly enhances brand loyalty, word-of-mouth communication, and emotional investment, offering numerous business benefits. It fosters long-term loyalty, with consumers resisting competitors, particularly in highly competitive markets [38]. Additionally, Brand Love drives spontaneous word-of-mouth, which helps expand brand influence at a low cost while increasing consumer trust [45]. It also boosts financial performance by increasing consumers' willingness to pay a premium [46] and reduces customer churn, thus lowering acquisition costs [47]. During crises, Brand Love encourages consumer tolerance and support, mitigating negative impacts and helping restore trust [48]. In conclusion, Brand Love strengthens loyalty, financial performance, and crisis management, providing a competitive advantage and ensuring long-term business growth. 2.5. Green Innovation Products Developed through eco-conscious innovation activities, green innovation products have become strategic tools for sustainability-driven enterprises. They fulfil environmental responsibilities and deliver competitive differentiation by aligning with regulatory standards and appealing to environmentally conscious consumers [49]. By demonstrating social responsibility, these products strengthen brand image and consumer trust, fostering loyalty [50]. Although they often require higher R&D investment, green innovation unlocks new markets, enables premium pricing, and enhances long- term profitability [51]. Operational and compliance costs are reduced through improved resource efficiency, waste minimization, and access to regulatory incentives [52]. Furthermore, green innovation enhances corporate resilience to climate change and resource scarcity, aligning business objectives with broader economic, social, and environmental goals [53]. Finally, by meeting the growing expectations of investors, governments, and communities, green products bolster stakeholder relationships, legitimacy, and future growth opportunities [54]. 2.5.1. Relevant Research on Green Innovation Environmentally conscious consumers are increasingly drawn to green products that align with their ethical values, influenced by transparent and credible information [55, 56]. Green innovation enhances enterprise performance by improving brand image, customer satisfaction, financial outcomes, and operational efficiency, particularly through green supply chain practices [57]. Technological innovation is key in driving green product development, enabling energy efficiency and using renewable materials while being supported by environmental policies and growing consumer demand. Moreover, green innovation strengthens market competitiveness by helping firms meet environmental standards, 1457 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate capture market share, and enhance brand performance through effective green marketing strategies [58]. 2.6. Perceived Uniqueness Perceived uniqueness is a critical strategic asset, enabling firms to differentiate their brands through distinctive features, innovative design, and sustainable value propositions [59]. In sectors like electric vehicles, uniqueness derived from green innovation not only appeals to environmentally conscious consumers but also strengthens brand positioning and appeal [60]. By fostering emotional attachment and brand love, perceived uniqueness drives positive word-of-mouth, enhances customer loyalty, and reduces churn [61]. It also underpins premium pricing strategies by reinforcing perceptions of quality and scarcity, increasing consumers’ willingness to pay and boosting profitability [62]. When combined with green social responsibility, uniqueness further elevates brand acceptance and consumer trust [63]. In marketing communications, highlighting unique attributes captures attention, reinforces brand identity, and amplifies messaging effectiveness [64]. Overall, perceived uniqueness is indispensable for sustaining competitive advantage in green innovation contexts and should be deliberately embedded in product design, brand strategy, and outreach efforts. 2.6.1. Relevant Research on Perceived Uniqueness Perceived uniqueness, the sense of being distinct within a social context, plays a crucial role in individual psychology, behaviour, and social adaptation. It is closely linked to self-identity and self- esteem, with research showing positive associations with self-efficacy, well-being, and life satisfaction [65]. According to the Optimal Distinctiveness Theory, individuals strive to balance social belonging with the desire for distinctiveness. Empirical studies support that perceived uniqueness enhances group innovation and creativity, particularly when individuals recognize their unique contributions [66]. Cultural and personality factors influence perceived Uniqueness: Western cultures prioritize individual distinctiveness, while collectivist cultures balance group conformity with situational uniqueness. Traits such as openness and intrinsic motivation are also positively correlated with higher perceived Uniqueness [67]. Additionally, perceived uniqueness aids social adaptation by promoting resilience, reducing psychological distress, and enhancing performance in competitive environments [68]. Overall, perceived uniqueness is critical in psychological health, social functioning, and adaptability, underscoring the need for further cross-cultural research on its implications. 2.7. Brand Acceptance Brand acceptance refers to the extent to which consumers recognize, embrace, and adopt a brand's products or services, serving as a crucial indicator of market success. Various factors influence it, including brand image, trust, personality, cultural context, and social media engagement. A robust brand image shaped by emotional connections, visual identity, and compelling storytelling enhances consumer trust and satisfaction, fostering acceptance [69]. Trust, grounded in perceived quality, transparency, and social responsibility, is especially vital in digital contexts, where responsiveness and communication influence consumer perceptions [70]. Brand personality, as articulated in Aaker [71] five-dimensional model, plays a significant role in consumer alignment and loyalty, with sincerity and emotional resonance being particularly influential [67]. Cultural differences also affect brand acceptance, with Western consumers often favouring personalized experiences, while Eastern consumers place a higher value on social reputation and collective identity. Social media and user-generated content are also pivotal in boosting brand trust and engagement, especially among younger audiences [72]. In conclusion, brand acceptance is a multifaceted construct that requires a strategic alignment of brand image, values, and communication efforts across diverse consumer segments and cultural contexts. 1458 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate 2.8. Green Social Responsibility Green Social Responsibility (GSR) has emerged as a crucial aspect of corporate strategy, reflecting a company's commitment to environmental stewardship and social welfare through sustainable practices. Research demonstrates that GSR positively impacts long-term performance, brand image, and stakeholder relations. By implementing green initiatives, companies can enhance their reputation and credibility, gaining a competitive edge in increasingly eco-conscious markets [39]. GSR strengthens consumer loyalty by cultivating emotional connections and trust, as environmentally responsible companies are more likely to attract and retain customers willing to support them, even at premium prices [65]. It also increases investor appeal, as strong environmental, social, and governance (ESG) performance reduces environmental risks and improves financial sustainability, attracting greater social capital [73]. Furthermore, GSR enhances employee morale, satisfaction, and retention by fostering a sense of purpose and alignment with sustainability values, making companies more attractive to talent committed to environmental responsibility [74]. As a key component of Corporate Social Responsibility (CSR), GSR focuses on environmental protection and promoting sustainable development. It improves brand value, market competitiveness, and financial outcomes by bolstering brand reputation, fostering consumer trust, and differentiating companies in the marketplace [75]. Studies show that GSR contributes to better financial performance by increasing resource efficiency, reducing costs, and mitigating environmental risks [76]. GSR also influences consumer behaviour, with consumers more likely to support environmentally responsible companies, enhancing brand loyalty and purchase intent [65]. Internally, GSR boosts employee motivation and satisfaction, cultivating a sense of pride and belonging and attracting talent aligned with sustainability values [77]. 2.9. Research Framework This study develops its research framework through an extensive literature review and formulating clear research objectives. The primary focus is on green innovation products, particularly new energy vehicles and their relationship with brand love. A questionnaire survey method is employed to investigate these relationships empirically. The research framework outlined in the preceding discussion is illustrated in Figure 1. Figure 1. Research Framework. 1459 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate 3. Methodology 3.1. Sampling Technique This study employed a stratified random sampling technique to ensure the reliability and representativeness of the sample. China was divided into five geographical strata—East, South, Central, West, and North with Shandong, Guangdong, Anhui, Sichuan, and Hebei selected as representative provinces. Each stratum was chosen to reflect distinct economic, industrial, and demographic characteristics that influence the adoption of new energy vehicles (NEVs). The target population comprised consumers who had purchased NEVs from five leading brands. Within each province, participants were randomly selected using lists provided by local dealerships, ensuring balanced representation across both regions and brands. This approach minimized sampling error, enhanced group comparability, and improved the overall accuracy and generalizability of the study's findings. 3.2. Sample Size The study’s sample was drawn using a stratified random sampling method targeting NEV consumers from five provinces (Shandong, Guangdong, Anhui, Sichuan, and Hebei) and across five major brands (BYD, SAIC, NIO, Li Auto, and Xiaomi). Two thousand one hundred questionnaires were distributed, with proportional sampling based on each province’s population size and NEV ownership rates. Data were collected through in-person visits to sales centres and online distribution via local agents, with strict measures to ensure participant confidentiality. The survey was conducted in two phases: the first phase focused on demographics, green innovation products, and brand love, while the second phase measured brand acceptance and green social responsibility. After excluding invalid responses, 1,667 valid questionnaires were retained, yielding an effective rate of 87.92% from the 1,896 responses received (90.29% response rate). The large, geographically diverse sample strengthens the study's external validity and provides valuable insights for academic research and industry practices. 3.3. Data Collection Tool Data were gathered using a structured questionnaire divided into two sections. The first section collected demographic information, including gender, age, education, marital status, occupation, annual income, and province, preceded by an introductory survey statement. The second section contained the core measurement items assessing green innovation products, perceived uniqueness, brand acceptance, brand love, and green social responsibility. All measurement scales were adapted from established academic studies and recognized for reliability and validity. The questionnaire was refined through consultations with an academic advisor, two PhD experts, and industry HR professionals. A rigorous forward-and-backwards translation process was applied to translate the English scales into Chinese to ensure linguistic and cultural accuracy. All core items (excluding demographics) were measured using a 7-point Likert scale to enhance clarity and reduce the likelihood of ambiguous responses. 3.4. Data Analysis Technique The study employed a comprehensive data analysis approach using SPSS and Mplus software. Hypotheses were tested through Structural Equation Modeling (SEM). Descriptive statistics were first used to summarize the key characteristics of the data. Reliability was assessed using Cronbach's alpha, with values above 0.700 considered acceptable. Validity was examined through convergent and discriminant validity assessments supported by Confirmatory Factor Analysis (CFA). Pearson correlation analysis was used to evaluate relationships between variables, while multicollinearity was checked using the Variance Inflation Factor (VIF), with acceptable values below 5. Harman's single- factor test and a common latent factor approach were applied to address Common Method Bias. Non- response bias was tested by comparing early and late respondents using Chi-square tests. SEM was then used to analyze the causal paths between latent constructs. A pilot survey was conducted between March 15 and May 18, 2024, resulting in 769 responses. After removing 112 invalid questionnaires—identified through logical inconsistencies, patterned 1460 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate responses, or errors—657 valid responses were retained for further analysis. To assess structural validity, these were used to conduct exploratory factor analysis (EFA). Items failing to meet factor loading standards were deleted to improve the instrument's robustness. Reliability was further tested using a subset of 101 responses analyzed via SPSS 25.0. Internal consistency was assessed using Cronbach's alpha, with values ≥0.70 deemed acceptable and ≥0.80 considered ideal. Items with Corrected Item-Total Correlation (CITC) values greater than 0.5, and those that increased Cronbach's alpha upon deletion were retained. To confirm the suitability of the data for factor analysis, the Kaiser- Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity were conducted. A KMO value closer to 1 indicates strong correlations among variables, making factor analysis appropriate. According to Kaiser and Rice [78] KMO values above 0.9 are considered excellent, above 0.8 good, above 0.7 acceptable, and below 0.5 unsuitable for factor analysis. A Bartlett’s test significance value below 0.05 further confirmed that the data were suitable for factor analysis, ensuring the robustness of the measurement model. 4. Result and Analysis 4.1. Descriptive Analysis Table 1 shows that the sample, consisting of 1,667 valid responses, was analyzed for key demographic characteristics. Gender distribution was nearly balanced, with 50.75% males and 49.25% females. Age-wise, most respondents were between 31-40 years (48.65%) and 20-30 years (29.39%). Regarding education, 43.49% held a bachelor's degree, and 23.04% had a graduate degree or higher. Regarding marital status, 59.45% were married. Occupation-wise, 21.90% worked in Government/public institutions, and 20.94% were freelancers. Income levels were primarily concentrated below 100,000 (25.25%) and 100,001-200,000 (43.01%). Geographically, most respondents came from Guangdong (29.45%) and Shandong (23.52%) provinces. This diverse sample ensures the study's reliability and applicability. Table 1. Descriptive Analysis. Characteristic Item Description Sample Size Percentage (%) Gender Male 846 50.75% Female 821 49.25% Age 20-30 years 490 29.39% 31-40 years 811 48.65% 41-50 years 200 11.99% 51 years and above 166 9.96% Education Level Associate Degree 558 33.47% Bachelor’s Degree 725 43.49% Graduate or above 384 23.04% Marital Status Single 676 40.55% Married 991 59.45% Occupation Corporate/Company Employee 310 18.60% Government/Public Institution Employee 365 21.90% Professional (e.g., teacher, doctor, lawyer, engineer) 299 17.94% Freelancer/Entrepreneur 349 20.94% Student 271 16.26% Other 73 4.38% Personal Annual Income Below 100,000 421 25.25% 100,001-200,000 717 43.01% 200,001-300,000 369 22.14% Above 300,000 160 9.60% Province Shandong Province 392 23.52% Guangdong Province 491 29.45% Anhui Province 237 14.22% Sichuan Province 277 16.62% Hebei Province 270 16.20% 1461 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate 4.2. Socioeconomic Factor Analysis Table 2 provides the distribution analysis for the variables Green Innovation Products, Brand Love, Perceived Uniqueness, Brand Acceptance, and Green Social Responsibility. Data are normally distributed if the absolute skewness is less than 5 and the absolute kurtosis is less than 10. The skewness values range from –0.063 (Green Innovation Products) to –1.013 (Green Social Responsibility), and the kurtosis values range from –0.889 (Green Innovation Products) to 1.408 (Green Social Responsibility). All values fall within the acceptable thresholds, indicating that the data are approximately normally distributed. It confirms the suitability of the dataset for further statistical analysis and supports the overall reliability of the research findings. Table 2. Socioeconomic Factor Analysis. Variable Mean Std. Dev. Skewness Kurtosis Green Innovation Products 4.197 1.153 -0.063 -0.889 Brand Love 4.672 1.233 -0.587 -0.332 Perceived Uniqueness 4.823 1.314 -0.896 0.231 Brand Acceptance 4.702 1.342 -0.609 -0.389 Green Social Responsibility 4.789 1.112 -1.013 1.408 Note: The data source is based on the results of the SPSS analysis. 4.3. Correlation Analysis Pearson's correlation method was employed to examine the relationships between the variables and their strengths, which also aids in understanding potential causal relationships. The Pearson correlation coefficient (r) ranges from -1 to 1, with values closer to 1 indicating a stronger relationship between the variables. Specifically, an r ≥ 0.7 denotes a high correlation, 0.3 ≤ r < 0.7 represents a moderate correlation, and r < 0.3 indicates a low correlation. A correlation of 0 implies no relationship, while positive and negative values suggest the direction of the relationship. As shown in Table 3, the correlation results reveal significant relationships (p < 0.05) between most variables, except for the moderating variable, Green Social Responsibility. Table 3. Correlation Analysis. Variable Green Innovation Products Brand Love Perceived Uniqueness Brand Acceptance Green Social Responsibility Green Innovation Products 1 Brand Love 0.279** 1 Perceived Uniqueness 0.190** 0.368** 1 Brand Acceptance 0.325** 0.395** 0.373** 1 Green Social Responsibility 0.051* 0.132** 0.091** 0.165** 1 Note: *p < 0.5, **p < 0.01, **p ≤ 0.001. Note 2: The data source is based on this study's compilation. 4.4. Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) was conducted using MPLUS 8.3 to validate the relationships among the study variables. SEM integrates factor and path analysis to examine measurement and structural models within a unified framework. 4.4.1. Confirmatory Factor Analysis (CFA) The measurement model, an essential component of SEM, was assessed through Confirmatory Factor Analysis (CFA) to evaluate the reliability and validity of the constructs. CFA specifically tests the convergent and discriminant validity of the variables. The measurement model’s adequacy was assessed using multiple fit indices, including the chi- square/degrees of freedom ratio (χ²/df), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root 1462 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Following the guidelines by Hu and Bentler [80] and Hair, et al. [81] the model fit was considered acceptable if χ²/df < 5, CFI > 0.90, TLI > 0.90, RMSEA < 0.08, and SRMR < 0.08. All standardized factor loadings exceeded the recommended threshold of 0.5 [79] indicating satisfactory convergent validity across the constructs. The results of the CFA for each construct are summarized in Table 4. All measurement models demonstrated good model fit according to the recommended thresholds. These results confirm that the constructs in this study exhibit strong convergent validity, and the measurement model is reliable for subsequent structural analysis. Table 4. Summary of Confirmatory Factor Analysis for Each Construct. Variable Item Factor Loading χ2/df CFI TLI RMSEA SRMR Brand Love AL1 0.846 1.893 0.999 0.998 0.023 0.007 AL2 0.832 AL3 0.83 AL4 0.797 AL5 0.798 AL6 0.818 Green Innovation Products CX1 0.753 3.982 0.991 0.988 0.042 0.015 CX2 0.752 CX3 0.748 CX4 0.735 CX5 0.738 CX6 0.739 CX7 0.726 CX8 0.749 Perceived Uniqueness ZJ1 0.822 1.479 0.999 0.999 0.017 0.006 ZJ2 0.837 ZJ3 0.83 ZJ4 0.819 ZJ5 0.798 ZJ6 0.813 Brand Acceptance JS1 0.848 2.613 0.999 0.998 0.031 0.005 JS2 0.875 Green Social Responsibility JS3 0.884 2.531 0.996 0.994 0.03 0.011 JS4 0.883 JS5 0.891 JS6 0.898 ZR1 0.728 ZR2 0.695 ZR3 0.687 ZR4 0.714 ZR5 0.722 ZR6 0.724 4.4.2. Structural Model and Hypothesis Testing This study employs MPLUS software for hypothesis testing. According to Hu and Bentler [80] and Hair, et al. [81] model fit is considered good if the following threshold criteria are satisfied: χ²/df < 5; TLI > 0.90; CFI > 0.90; RMSEA < 0.08; SRMR < 0.08. Meeting these criteria indicates a good fit between the data and the hypothesized measurement model. 1463 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate 4.5. Empirical Results of Direct Effects Analysis Direct effects were analyzed in three separate models, each examining different paths between independent and dependent variables. 4.5.1. Model 1: Green Innovation Products Regressed on Perceived Uniqueness Figure 2 shows the direct effect analysis for Model 1, where Green Innovation Products are regressed on Perceived Uniqueness. The model fit indices demonstrate an excellent fit: χ²/df = 1.843 (less than 5), CFI = 0.995, TLI = 0.994 (both greater than 0.90), RMSEA = 0.022 (less than 0.08), and SRMR = 0.015 (less than 0.08). These indices confirm a good fit between the hypothesized model and the observed data. The standardized path coefficient from Green Innovation Products to Perceived Uniqueness is 0.227 and is statistically significant (p = 0.000 < 0.05). It indicates that Green Innovation Products significantly positively influence perceived uniqueness. Figure 2. Direct Effects Analysis Model 1. 4.5.2. Model 2: Green Innovation Products and Perceived Uniqueness Regressed on Brand Acceptance Figure 3 illustrates Model 2, which examines the direct effects of Green Innovation Products and Perceived Uniqueness on Brand Acceptance. The model fit indices are strong: χ²/df = 1.502 (less than 5), CFI = 0.996, TLI = 0.996 (both above 0.90), RMSEA = 0.017 (less than 0.08), and SRMR = 0.014 (less than 0.08). These results indicate that Model 2 has an excellent fit.The analysis reveals that the standardized path coefficient from Green Innovation Products to Brand Acceptance is 0.272, which is significant (p = 0.000 < 0.05), suggesting a positive effect. Additionally, the standardized path coefficient from Perceived Uniqueness to Brand Acceptance is 0.340, also significant (p = 0.000 < 0.05), confirming that Perceived Uniqueness positively affects Brand Acceptance. 1464 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Figure 3. Direct Effects Analysis Model 2. 4.5.3. Model 3: Green Innovation Products, Perceived Uniqueness, and Brand Acceptance Regressed on Brand Love Figure 4 presents Model 3, where the paths from Green Innovation Products, Perceived Uniqueness, and Brand Acceptance to Brand Love are examined. The model fit indices again show a strong fit: χ²/df = 1.300 (less than 5), CFI = 0.997, TLI = 0.997 (both above 0.90), RMSEA = 0.013 (less than 0.08), and SRMR = 0.014 (less than 0.08). The results indicate that the standardized path coefficient from Green Innovation Products to Brand Love is 0.162, and it is significant (p = 0.000 < 0.05), showing a positive relationship. Moreover, Perceived Uniqueness significantly influences Brand Love with a standardized coefficient of 0.262 (p = 0.000 < 0.05), and Brand Acceptance also significantly influences Brand Love with a standardized coefficient of 0.259 (p = 0.000 < 0.05). 1465 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Figure 4. Direct Effects Analysis Model 3. 4.5.4. Summary of Direct Effects Hypothesis Path Results Table 5 summarises the direct effect paths tested across the three models. The analysis reveals that the path from Green Innovation Products to Perceived Uniqueness has a standardized coefficient of 0.227 (t = 7.758, p = 0.000), with an explained variance (R²) of 0.043, indicating that Green Innovation Products account for 4.3% of the variance in Perceived Uniqueness. Similarly, the path from Green Innovation Products to Brand Acceptance shows a standardized coefficient of 0.272 (t = 11.653, p = 0.000), with an R² of 0.228, suggesting that Green Innovation Products explain 22.8% of the variance in Brand Acceptance. The path from Perceived Uniqueness to Brand Acceptance is also significant, with a coefficient of 0.340 (t = 15.004, p = 0.000); however, no separate R² value is reported for this path, as it shares explained variance with Green Innovation Products in predicting Brand Acceptance. In the final model predicting Brand Love, the path from Green Innovation Products to Brand Love shows a standardized coefficient of 0.162 (t = 6.458, p = 0.000) with an R² value of 0.262, indicating that the model explains 26.2% of the variance in Brand Love. Additionally, the path from Perceived Uniqueness to Brand Love presents a coefficient of 0.262 (t = 10.464, p = 0.000), while the path from Brand Acceptance to Brand Love has a coefficient of 0.259 (t = 10.012, p = 0.000). Overall, all direct effects are statistically significant at the 0.05 level, and the structural models demonstrate a good fit based on established standard fit indices. These results collectively support the hypotheses, confirming that Green Innovation Products positively influence Perceived Uniqueness, Brand Acceptance, and Brand Love, with Perceived Uniqueness and Brand Acceptance further strengthening the relationship with Brand Love. 1466 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Table 5. Summary of Direct Effect Hypothesis Path Results. Hypothesis Path Standardized Path Coefficient S.E. t P R² Green Innovation Products → Perceived Uniqueness 0.227 0.029 7.758 0.000 0.043 Green Innovation Products → Brand Acceptance 0.272 0.023 11.653 0.000 0.228 Perceived uniqueness → Brand Acceptance 0.340 0.023 15.004 0.000 Green Innovation Products → Brand Love 0.162 0.025 6.458 0.000 0.262 Perceived uniqueness → Brand Love 0.262 0.025 10.464 0.000 Brand Acceptance → Brand Love 0.259 0.026 10.012 0.000 5. Conclusion and Recommendation This study demonstrates that green innovation products significantly enhance brand love through perceived uniqueness, brand acceptance, and green social responsibility. These findings confirm the role of sustainable innovation in fostering emotional consumer-brand connections and enhancing brand differentiation. Perceived uniqueness and brand acceptance mediate the relationship between green innovation and brand love. In contrast, green social responsibility moderates this connection, emphasizing the importance of ethical alignment in strengthening consumer relationships. Companies that integrate green innovation into their products and align their sustainability efforts with social responsibility are likelier to cultivate lasting emotional bonds with their customers. For businesses, the key recommendation is to prioritize technological and environmental differentiation in product development. By offering products that stand out due to their innovative, eco- friendly features, brands can foster a unique identity and emotional connection with consumers. Furthermore, companies should consider adopting genuine and consistent corporate social responsibility (CSR) practices, viewing CSR as a strategic asset rather than a marketing tool. This approach will not only build trust but also enhance brand loyalty. Additionally, leveraging data-driven marketing tools to communicate green initiatives more effectively will help brands connect with their environmentally conscious consumer base. Integrating emerging technologies like AI, big data, and blockchain into sustainability efforts can enhance transparency, improve consumer engagement, and drive more profound brand love. 6. Limitations and Future Studies While this study provides a theoretical framework linking green innovation products to brand love through perceived uniqueness, brand acceptance, and green social responsibility, several limitations must be noted. Focusing solely on the electric vehicle sector and Chinese consumers limits the generalizability of the findings. The use of self-reported data may introduce social desirability bias, and key factors such as personal environmental values, green product knowledge, and environmental awareness were not considered. Future research should expand to other industries, such as consumer electronics, sustainable fashion, green buildings, and food, and adopt cross-cultural comparisons to explore cultural moderators like individualism and long-term orientation. Longitudinal studies are also needed to assess the lasting effects of corporate social responsibility on trust and loyalty. Finally, integrating emotional decision-making and consumer psychology theories could offer deeper insights into green brand attachment. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. 1467 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 1453-1470, 2025 DOI: 10.55214/25768484.v9i5.7177 © 2025 by the authors; licensee Learning Gate Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] T. Liu and Y. C. 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