# Transforming Green Transparency into Green Brand Loyalty and Repurchase Intentions: The Role of Brand Image and Credibility among Electric Vehicle Users

Ashish Ashok Uikey, Ruturaj Baber, Zericho R Marak

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Ashish Ashok Uikey, Ruturaj Baber, Zericho R Marak. Transforming Green Transparency into Green Brand Loyalty and Repurchase Intentions: The Role of Brand Image and Credibility among Electric Vehicle Users. Journal of Applied Structural Equation Modeling, 2025, 9 (1), pp.1-24. 10.47263/JASEM.9(1)02. hal-04925852

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TRANSFORMING GREEN TRANSPARENCY INTO GREEN BRAND LOYALTY AND REPURCHASE INTENTIONS: THE ROLE OF BRAND IMAGE AND CREDIBILITY AMONG ELECTRIC VEHICLE USERS

Ashish Ashok Uikeyᵃ*, Ruturaj Baberᵇ and Zericho R. Marakᶜ

ᵃSymbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

ᵇSchool of Business and Management, Christ University, Bengaluru, India

ᶜashishashokuikey@gmail.com

ABSTRACT

The present study leverages the Stimulus-Organism-Behavior-Consequence (SOBC) framework to investigate how green transparency influences green brand loyalty and repurchase intention among electric vehicle consumers. Specifically, it examines the mediating roles of brand image and brand credibility in the relationships between green transparency, green brand loyalty, and repurchase intention. Data collected from 386 electric vehicle users were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results reveal that green transparency positively impacts brand image and brand credibility, which subsequently enhances green brand loyalty and repurchase intention. Mediation analysis further highlights brand image and brand credibility as critical mechanisms linking green transparency to green brand loyalty. This study extends the SOBC framework to green marketing, offering theoretical and practical insights into fostering sustainable consumer behavior. By emphasizing the role of green transparency in building credible and compelling brand narratives, the findings guide marketers in cultivating consumer trust and loyalty while supporting policymakers in formulating transparency regulations for a sustainable marketplace.

**Keywords:** Brand credibility; Brand image; Electric vehicles; Green brand loyalty; Green marketing; Green transparency; Repurchase intention

INTRODUCTION

The growing consumer shift toward sustainable products and lifestyles stems largely from perceived environmental benefits, which support ecosystem preservation (Maduku, 2024). In response, businesses are increasingly emphasizing eco-friendly products to meet this demand (Saeed et al., 2023). Climate change, driven by air pollution and greenhouse gas emissions, remains a pressing global issue, with the transportation sector contributing 37% of CO₂ emissions (IEA, 2022). Electric vehicles offer a promising solution by emitting no air pollutants during operation, unlike traditional combustion-engine vehicles (Zaino et al., 2024). Recognized as cleaner alternatives, electric vehicles significantly reduce environmental harm (Costa et al., 2021). Electric vehicles are particularly critical for India's push toward sustainable mobility. The country urgently requires energy-efficient and cleaner vehicles to secure a sustainable automotive future (Singh et al., 2021). Recognizing this need, the Indian government is working to reduce
its reliance on imported energy, lower greenhouse gas emissions, and mitigate the adverse effects of transportation, such as global warming (Goel et al., 2021).

The global vehicle market is undergoing a transformation, with electric vehicles increasingly replacing conventional vehicles (Lee et al., 2023). In October 2024, India recorded 109,096 new electric vehicles registrations, the highest monthly figure of the year (Ministry of Road Transport and Highways, 2024). By July 29, 2024, cumulative electric vehicles registrations on the e-Vahan portal had reached 4,574,938, marking a 42.06% increase in Financial Year 2023-24 compared to the previous year (Press Information Bureau, 2024). After more than a decade of adoption of electric vehicles, many early adopters are now replacing their initial purchases (Lee et al., 2023).

To achieve its target of 100% electric vehicles sales, India must ensure sustained adoption through repeat purchases (Goel et al., 2021). If electric vehicles owners revert to vehicles other than electric vehicles for subsequent purchases, market penetration goals will falter. Identifying early adopters dubbed "innovators" in product diffusion theory is crucial, as they influence broader adoption patterns (Dua et al., 2024). Additionally, customer retention is critical for an organization's long-term success, as retaining existing customers is five times more cost-effective than acquiring new ones (Zietsman et al., 2019). This underscores the importance of fostering loyalty among early electric vehicle buyers to sustain momentum and drive future growth (Leong et al., 2024).

Transparency in green initiatives is essential for driving large-scale industry shifts toward improved environmental conditions and corporate social responsibility (Lee & Chen, 2019). With green brands often priced higher than conventional alternatives, consumers increasingly demand detailed information to justify their purchases (Lin et al., 2017a). This is particularly relevant for electric vehicles brands, which command premium prices. In India, government policies are fostering electric vehicles adoption, setting the stage for industry growth (Chaturvedi et al., 2022). To meet rising consumer expectations, brands must provide precise information in their environmental claims, ensuring that green products deliver value beyond conventional options (Uikey & Baber, 2023).

As businesses leverage green branding to strengthen brand image, understanding how environmental transparency influences consumer perceptions becomes crucial (Mehraj & Qureshi, 2022). In the sustainability era, enhancing brand image to align with environmental priorities has become a strategic imperative (Lee & Chen, 2019). Green transparency significantly impacts brand image by fostering trust through transparency, especially when brands comply with regulations (Mim et al., 2022). Positive brand image perceptions often translate into higher consumer preference (Chen et al., 2020). Moreover, brand image mediates the link between environmental concerns and brand loyalty (Watson et al., 2024). However, limited research explores the role of green transparency in shaping brand image. For example, while Arham and Dwita (2021) emphasize the importance of brand trust in connecting brand image to green brand loyalty, they overlook how green transparency can strengthen trust and enhance brand image. This gap underscores the need to investigate the mediating role of brand image between green transparency and green brand loyalty.

Consumer skepticism about brands' ethical commitments highlights the growing importance of brand credibility, which denotes a brand's trustworthiness and ability to fulfill its claims (Erdem & Swait, 2004). Heightened doubts about the authenticity of green claims complicate purchasing decisions, as eco-marketing efforts are often met with suspicion (Szabo & Webster, 2021; Thøgersen, 2021). Research shows that transparent information positively influences consumer attitudes toward sustainable products (Mim et al., 2022). Green transparency has been shown to enhance brand credibility (Sansome et al., 2024), yet how brand credibility mediates the relationship between green transparency and green brand loyalty remains underexplored. While
transparency can enhance credibility, the pathway by which this fosters loyalty in sustainable branding contexts remains unclear. Most studies address these constructs independently, leaving their interconnected dynamics within a mediation framework largely unexamined (Yazdanparast & Kukar-Kinney, 2023).

Brand loyalty, particularly green brand loyalty, emerges from consumers' deep connection to a brand due to shared environmental values (Chen et al., 2020). This loyalty becomes vital in competitive markets, where it fosters consistent brand preference and reduces susceptibility to rival marketing campaigns (Sohaib & Han, 2023). Repurchase intention is commonly used to measure brand loyalty (Zhang & Bloemer, 2008) and serves as an indicator of a company's long-term viability (Hwang et al., 2021). Strong emotional bonds with a brand, especially in service contexts, tend to heighten consumer loyalty, leading to repeat purchases and greater revenue (Jang, 2021).

This study's research questions are based on the SOBC framework. In the SOBC model, authors argue that green transparency acts as the stimulus, enhancing brand image and brand credibility (Organism), which then influences consumer behavior toward green brand loyalty (Behavior), ultimately leading to repurchase intention (Consequence). Previous research has mainly focused on general green practices, overlooking the specific challenges and expectations of the electric vehicles industry, such as heightened consumer skepticism and the need for transparency. Uikey and Baber (2023) examined antecedents of green brand loyalty in electric vehicles using the SOR framework, while Lee et al. (2023) studied electric vehicles replacement dynamics. Hasan (2021) and Kwon et al. (2020) used surveys to assess electric vehicles repurchase intentions in Norway and South Korea. By addressing these gaps, this study broadens the SOBC framework's application and offers deeper insights into consumer decision-making in green marketing, providing a solid foundation for advancing theory and practice in green consumer behavior.

The study carried out the following research objectives (RO), RO1: To examine the extent to which green transparency influences brand image and brand credibility, RO2: To investigate whether brand image and brand credibility mediate the relationship between green transparency and green brand loyalty, RO3: To assess the impact of green brand loyalty on consumers' repurchase intention, RO4: To evaluate the applicability of the SOBC framework in explaining the relationships between green transparency, brand image, brand credibility, green brand loyalty, and repurchase intention in the context of electric vehicles.

Despite significant research on green consumer behavior, key gaps remain, particularly regarding factors influencing green brand loyalty and repurchase intention in electric vehicles. While the roles of green transparency, brand image, and brand credibility have been widely studied, their integrated examination within the SOBC framework for Electric Vehicles is lacking. The literature has yet to explore how green transparency influences intermediary factors like brand image and brand credibility (Organism) to shape consumer behavior and its consequences (Deng & Yang, 2022). Additionally, the mediating roles of brand image and brand credibility in connecting green transparency with green brand loyalty have received limited attention, especially in the electric vehicles market where trust and perception are critical (Rahman & Nguyen-Viet, 2023; Sansome et al., 2024). Although studies highlight the importance of brand loyalty for repurchase behavior (Pancic et al., 2023), few have explored how loyalty to sustainable brands drives consistent consumer actions, particularly for high-involvement products like Electric Vehicles (Hasan, 2021). The dynamic relationships among these constructs, within the SOBC framework, remain underexplored in both theoretical and empirical contexts. Previous studies have applied the SOBC framework to areas like unconventional purchasing (Laato et al., 2020), buying intentions (Chakraborty et al., 2022), and eco-friendly clothing (Dhir et al., 2021a), but this research will use it to investigate repurchase intentions of electric vehicles.
# THEORETICAL BACKGROUND AND HYPOTHESIS DEVELOPMENT

## Stimulus-Organism-Behavior-Consequence (SOBC) Framework

The SOBC framework offers a clear advantage over the traditional SOR model in analyzing consumer behavior in the electric vehicles sector, providing a deeper understanding of loyalty and repurchase intentions (Uikey, 2024). The SOR model, developed by Mehrabian and Russell (1974), examines the interaction between stimuli (S), organism (O), and responses (R), but does not address the long-term consequences of behavior. The SOBC model, based on Bandura's (1977) social learning theory, builds on the SOR framework by adding the dimension of consequences (C), highlighting how specific behaviors lead to outcomes. This enhancement allows a fuller exploration of consumer behavior, particularly the long-term effects of loyalty, like repurchase intentions.

Stimulus (S) refers to external factors or environmental cues that trigger consumer responses. In this study, green transparency serves as the stimulus by influencing consumer perceptions through transparent environmental practices. Organism (O) represents the internal processes, including cognitive and affective evaluations that mediate the stimulus's impact on behavior. Here, brand image and brand credibility are considered organismic constructs reflecting consumer perceptions and trust. Behavior (B) denotes observable actions or reactions resulting from organismic evaluations. This study identifies green brand loyalty as the behavior driven by brand image and brand credibility. Consequence (C) refers to the outcomes or results of the behavior. In this study, repurchase intention is the consequence, showcasing consumer commitment to sustainable brands over time.

By considering internal motivations shaped by personal goals and cognitive processes (Chakraborty et al., 2022), the SOBC framework connects these motivations to the broader consequences of actions. This is particularly valuable in green marketing research, where it helps analyze how green transparency (Stimulus) affects brand image and brand credibility (Organism), leading to green brand loyalty (Behavior) and repurchase intention (Consequence). The SOBC framework, by integrating these elements, proves more effective for studying sustainable consumer behavior than the SOR model (Dhir et al., 2021b; Whelan et al., 2020).

## Green Transparency and Brand Image

Within the SOBC framework, green transparency serves as the stimulus, reflecting a brand's dedication to transparently sharing its environmental practices. By showcasing eco-friendly initiatives, brands communicate authenticity, ethical responsibility, and alignment with sustainability values (Deng & Yang, 2022). This transparency influences the organism, represented by brand image, by enhancing perceptions of trustworthiness, credibility, and alignment with consumer expectations (Busser & Shulga, 2019). In the electric vehicles market, where environmental sustainability is paramount, consumers closely evaluate a brand's green efforts. Transparent communication reassures them of the brand's adherence to environmental standards while strengthening emotional connections and perceptions of reliability (Lee & Chen, 2019). Transparency is critical for boosting brand image, positioning the brand as a leader in environmental responsibility and differentiating it in a competitive market (Phan et al., 2024). Given the electric vehicles sector's key role in combating climate change, understanding how green transparency shapes brand image is vital for strategies targeting eco-conscious consumers. By fostering loyalty through a credible and compelling brand image, brands can build lasting consumer engagement. Based on these insights, the following hypothesis is proposed:

*H1: Green transparency positively affects brand image*
## Green Transparency and Brand Credibility

Erdem and Swait (2004) suggest that brands demonstrating competence and providing valuable, accurate information are more likely to earn consumer trust. However, many consumers remain skeptical about whether brands truly uphold their ethical commitments (Guèvremont, 2019). This skepticism is especially prevalent in the electric vehicles industry, where environmental claims face intense scrutiny. Within the SOBC framework, green transparency acts as a stimulus, reducing information asymmetry and easing consumer doubts. Transparency requires brands to proactively share relevant, timely information, demonstrating accountability and reinforcing perceptions of their environmental and social commitments (Sansome et al., 2024). Conversely, insufficient transparency erodes stakeholder trust, alienates consumers, and damages brand reputation (Li et al., 2020). Brand credibility represents the organism, reflecting consumers' evaluations of a brand's trustworthiness and expertise. Transparency plays a key role in shaping consumer perceptions and building trust (Yazdanparast & Kukar-Kinney, 2023). Despite this, limited empirical evidence directly links green transparency to brand credibility, creating a knowledge gap on how electric vehicles brands can leverage transparency to enhance credibility. As sustainability-conscious consumers increasingly demand accountability, exploring how green transparency impacts brand credibility within the SOBC framework is essential. This analysis can provide strategic insights for electric vehicles brands aiming to build trust and succeed in an environmentally conscious market. Accordingly, we propose the following hypothesis:

### H2: Green transparency positively affects brand credibility

## Green Transparency and Green Brand Loyalty

Green transparency plays a crucial role in shaping consumer behavior, particularly in fostering green brand loyalty. Clear, credible, and detailed communication about an electric vehicle brand's sustainability practices drives trust and emotional connections with the brand. Transparency meets consumer expectations for authenticity and value alignment (Mim et al., 2024). By openly sharing sustainability initiatives, electric vehicles brands build trust, an essential foundation of loyalty (Kumar, 2022). Trust not only strengthens consumer-brand bonds but also provides a competitive edge, as retaining loyal customers is more cost-effective than acquiring new ones (Zeitsman et al., 2019). Furthermore, transparency creates emotional connections by reflecting shared values, reinforcing loyalty and encouraging advocacy (Rahman & Nguyen-Viet, 2023). For electric vehicles, marketed as environmentally friendly alternatives, consumers increasingly demand transparency in marketing claims, production processes, and lifecycle sustainability. A disconnect between consumer expectations and brand communications risks weakening loyalty and damaging brand equity (Copeland & Bhaduri, 2020). By prioritizing transparency, electric vehicles brands can align with consumer values, build trust, and cultivate long-term loyalty in a competitive market. Based on the SOBC model and role of green transparency as a stimulus influencing behavior, the following hypothesis is proposed:

### H3: Green transparency positively affects green brand loyalty

## Brand Image and Green Brand Loyalty

Within the SOBC framework, brand image serves as the organism, reflecting consumer perceptions shaped by the stimulus, which in this study is green transparency. Phan et al. (2024) emphasize that a strong brand image positively influences consumer attitudes and fosters loyalty, aligning the brand with consumer values. Similarly, Jung et al. (2020) and García-Salirrosas et al. (2024) highlight the role of brand image in creating emotional bonds that strengthen green brand loyalty. However, while the direct impact of brand image on green brand loyalty is well-
documented (Bashir et al., 2020; Chandra, 2019), the mechanisms linking these constructs in environmentally focused sectors, such as electric vehicles, are underexplored. In the electric vehicles market, brand image not only differentiates brands but also enhances their alignment with eco-conscious consumer identities, reinforcing trust, shared values, and authenticity (Lin et al., 2017b). Kartono and Warmika (2018) argue that loyalty transcends mere repurchase behavior, encapsulating deeper emotional connections and alignment with sustainability goals. Guided by the SOBC model, this hypothesis posits that the green transparency (Stimulus) enhances brand image (Organism), which in turn fosters green brand loyalty (Behavior). Thus, the interplay between emotional appeal and environmental alignment makes brand image a pivotal construct for cultivating green brand loyalty in sustainability-driven markets. Hence, we hypothesize:

H4: *Brand image positively affects green brand loyalty*

*Brand Credibility and Green Brand Loyalty*

Brand credibility represents the organism in the SOBC model, encapsulating consumer belief in the reliability of a brand's environmental claims. Green transparency (Stimulus) plays a crucial role in shaping brand credibility, as consumers increasingly demand evidence-based claims to reduce skepticism surrounding green marketing (Fatma & Khan, 2023). Brand credibility reinforces perceptions of value by mitigating uncertainty and validating a brand's environmental commitments (Mansoor & Paul, 2022). This is particularly critical in the electric vehicles sector, where heightened scrutiny of sustainability claims requires brands to consistently deliver on promises of quality and eco-friendliness (Adnan et al., 2019). Credible brands strengthen consumer trust, which subsequently enhances green brand loyalty by fostering confidence in purchasing decisions. Mansoor et al. (2022) argue that brand credibility alleviates information asymmetry, encouraging long-term loyalty through perceived authenticity. Within the SOBC framework, brand credibility mediates the relationship between green transparency and consumer behavior, translating transparent practices into sustained loyalty and advocacy. As consumers of electric vehicles prioritize sustainability, exploring the role of brand credibility in fostering green brand loyalty provides actionable insights for loyalty in green marketing. Based on this, the following hypothesis is proposed:

H5: *Brand credibility positively affects green brand loyalty*

*Green Brand Loyalty and Repurchase Intention*

Green brand loyalty represents the Behavior (B) in the SOBC model, driven by green transparency (S) and internal states like brand image and brand credibility (O). It manifests as a consistent preference for a brand due to its alignment with eco-conscious values, even amidst competing alternatives (Lin et al., 2017b). Green brand loyalty is a powerful predictor of repurchase intention, which represents the Consequence (C) in the framework. Pancic et al. (2023) underscore that eco-conscious loyalty significantly enhances repurchase intention, driven by emotional and ethical commitments to green products. Similarly, Jaiswal and Kant (2018) highlight that consumers with strong environmental values exhibit higher loyalty and repeat purchase behaviors. In the electric vehicles sector, where sustainability is central to consumer decisions, green brand loyalty serves as a critical behavioral outcome that directly influences repurchase intention. Understanding this relationship is essential for fostering long-term engagement and achieving sustainability objectives in the automotive market. Guided by the SOBC model, this hypothesis asserts that green brand loyalty translates into repurchase intention, providing a robust foundation for promoting sustainable consumption patterns in electric vehicles industry. Thus, the following hypothesis is proposed:
H8: *Green brand loyalty positively affects repurchase intention*

*Mediating role of Brand Image and Brand Credibility*

Consumers who view a brand as environmentally responsible tend to form stronger emotional connections, increasing loyalty (Bashir et al., 2020). Chandra (2019) underscores the importance of brand image in shaping consumer preferences and loyalty. The SOBC framework helps explain this: green transparency acts as the stimulus affecting perceptions, processed through brand image and brand credibility as the organism, leading to green brand loyalty as the behavior and repurchase intention as the outcome. Green transparency shapes perceptions of a brand's environmental responsibility, influencing brand image. When consumers see a brand as transparent in practices like sustainable production and ecological impact, they develop a positive brand image (Konuk et al., 2023). This fosters green brand loyalty by aligning with consumer values and concerns, promoting loyalty (Li et al., 2020). However, credibility matters too; transparency alone isn't enough, it must support brand credibility to build trust. Brand image reflects a brand's overall image, while brand credibility assesses the trustworthiness of environmental claims. Without transparency, brand image may seem deceptive, undermining loyalty (Cambier & Poncin, 2020). Brand image mediates the relationship between green transparency and green brand loyalty by reinforcing trust in the brand's sustainable practices (Arham & Dwita, 2021). While study by Kartono and Warmika (2018) emphasize role of brand image in loyalty, they overlook how brand credibility influences brand image. In today's eco-conscious market, brand credibility is critical in shaping brand image and loyalty, especially in the electric vehicles sector. Transparent and credible claims foster emotional attachment, driving higher green brand loyalty and repurchase intention (Konuk et al., 2023). Although research by Arham and Dwita (2021) explored trust's role in the green transparency-green brand loyalty link, less focus has been given to the independent and combined effects of brand image and brand credibility as mediators. Hence, this study proposes that:

H7: *Brand image partially mediates the relationship between green transparency and green brand loyalty*

H8: *Brand credibility partially mediates relationship between green transparency and green brand loyalty*

Figure 1 presents the hypothesized framework of the study.

<figure><img src="image_2.png" /><figcaption>Figure 1: Theoretical Framework Developed</figcaption></figure>

Source: Author(s) own creation
# METHODS

## Context

The vehicle market is undergoing a major shift, with electric vehicles becoming a key alternative to traditional fuel-powered cars, signaling progress toward sustainable transportation (Lee et al., 2023). The rise in electric vehicles sales highlights India's commitment to cutting greenhouse gas emissions and improving air quality. Achieving the ambitious target of 100% electric vehicles sales will depend on sustained consumer engagement and repeat purchases (Goel et al., 2021). Retaining current electric vehicles users and preventing them from switching back to traditional vehicles is critical for full market adoption. Early adopters significantly influence the purchasing decisions of later consumers (Dua et al., 2024). Understanding these groups and their loyalty to electric vehicles brands is essential for ensuring long-term sustainability in the automotive sector. This highlights the need to examine factors such as green transparency, brand image, brand credibility, and green brand loyalty driving repurchase intentions.

## Instruments

A structured questionnaire is often the preferred method for exploring and comprehending factors related to attitudes (Tourangeau et al., 2000). A questionnaire survey was used to collect data for analysis. The measurement scales utilized in this study were drawn from existing research. A comprehensive list of all the items employed in our research is given in Appendix 1. In total, 23 items were assessed using a 7-point Likert scale (except for demographic variables), ranging from 1 for "strongly disagree" to 7 for "strongly agree". Participants were asked to provide demographic details including age, gender, education, occupation and income.

A 4-item scale was adapted from Deng and Yang (2022) to measure green transparency. Green transparency, defined as a brand's commitment to openly sharing its environmental efforts, plays a pivotal role in this context (Palanski et al., 2011). A sample item includes, "The brand provides clear green element information". Deng and Yang (2022) reported the composite reliability of the instrument as 0.920.

A 4-item scale of brand image was adapted from Foroudi et al. (2018). Brand image is defined as the associations consumers hold about a brand, largely shaped by the brand's communicated values and practices (Chen et al., 2017). A sample item includes, "The image of company is consistent with how I like to see myself". Foroudi et al. (2018) reported the composite reliability of the instrument as 0.906.

A 3-item scale of brand credibility was sourced from Sekhar et al. (2022). Brand credibility refers to the extent to which consumers perceive a brand's intentions as trustworthy and reliable, including its ability and willingness to deliver on commitments, such as ethical claims (Erdem and Swait, 2004). A sample item includes, "This brand delivers what it promises". Sekhar et al. (2022) reported the composite reliability of the instrument as 0.81.

A 4-item scale of green brand loyalty was sourced from Chen (2013). Green brand loyalty refers to the tendency of customers to repeatedly choose a brand because of their strong environmental values and commitment to sustainability (Chen et al., 2020). A sample item includes, "I intend to continue buying this product because it is (environmentally) friendly". Chen (2013) reported the composite reliability of the instrument as 0.835.

A 3-item scale of repurchase intention was sourced from Sehgal et al. (2023). Repurchase intention refers to the customer's choice to continue interacting with a purveyor in the future and
the specific actions they might take (Hume et al., 2007). A sample item includes, "I will repurchase this green brand products again in the future". Sehgal et al. (2023) reported the composite reliability of the instrument as 0.856.

A pretest was conducted before the main data collection to ensure the clarity of all measurement items and to prevent misinterpretation by participants (Memon et al., 2023). Three subject matter experts with research experience reviewed the questionnaire, and necessary revisions were incorporated based on their feedback.

## *Sampling and data collection*

A descriptive research design was followed, and non-probability purposive sampling technique was utilized for this study. In purposive sampling the sample consists of people whom the researcher believes will find answers to the research problems (Lune & Berg, 2017). For inclusion in the study, respondents were required to hold at least a graduate degree. Furthermore, they were required to have an electric vehicle used for about one year to ensure adequate product experience. Finally, they were required to have a basic understanding of green practices, environmental concerns, or sustainability.

The study was conducted in Maharashtra, India, the second-highest state in electric vehicle ownership after Uttar Pradesh (Sen, 2024). Urban consumers were selected due to their higher exposure and education, which facilitated survey participation. Data was gathered over the duration of three months through visits to various electric vehicles showrooms. All of the respondents were assured of their anonymity and the confidentiality of their responses. Participation was voluntary, with no compensation offered, and respondents could skip questions or withdraw at any time. Questionnaires were distributed after confirming that respondents use an electric vehicle via screening questions such as, "Do you use an electric vehicle?" and "Do you know about the claims of the electric vehicle (Specific) Brand?" Verbal consent was obtained, and participants were free to select their preferred brands, as the study did not focus on any particular one. A total of 386 respondents, who were users of EVs, participated in the study through a structured questionnaire. This sample size was deemed adequate based on Kline's (2015) guidelines.

## *Common Method Bias*

The study addressed common method bias through several steps. Measures were taken to ensure respondent anonymity and confidentiality, aligning their responses with the research purpose (Memon et al., 2023). Participants were explicitly informed that there were no right or wrong answers, provided their responses were truthful. This approach aimed to reduce evaluation apprehension and minimize the likelihood of respondents altering their answers to appear more favorable (Podsakoff et al., 2003). Additionally, Harman's single factor test was applied. The factor analysis indicated that 21.655% of the total variance was explained by one factor. This is well below the 50% threshold, indicating that common method bias to be absent from the study (Podsakoff et al., 2006).

# DATA ANALYSIS AND RESULTS

## *Demographic profile of the respondents*

A comprehensive snapshot of the demographic profile of the respondents is mentioned in Table 1. In terms of age distribution, the majority fall within the 18-to-25-year age bracket. Gender-
wise, the sample is relatively balanced, with slightly more male respondents. Education levels vary, with a significant portion having completed either graduation or post-graduation. Occupationally, private employment appears to be the most common, followed by government employment and self-employment. Lastly, income distribution shows a fairly even split across income brackets, with a notable portion falling within the mid-range.

<table><caption>Table 1: Demographic Profile of the Respondent (n=386)</caption><thead><tr><th>Demographic Measures</th><th>Category</th><th>Frequency</th><th>Percentage</th></tr></thead><tbody><tr><td rowspan="4">Age</td><td>18 to 25 years</td><td>149</td><td>38.6</td></tr><tr><td>26 to 35 years</td><td>137</td><td>35.5</td></tr><tr><td>36 to 45 years</td><td>88</td><td>22.8</td></tr><tr><td>46 years and above</td><td>12</td><td>3.1</td></tr><tr><td rowspan="2">Gender</td><td>Male</td><td>200</td><td>51.8</td></tr><tr><td>Female</td><td>186</td><td>48.2</td></tr><tr><td rowspan="4">Education</td><td>Graduation</td><td>184</td><td>47.7</td></tr><tr><td>Post-Graduation</td><td>154</td><td>39.9</td></tr><tr><td>Ph.D.</td><td>26</td><td>6.7</td></tr><tr><td>Any Other (ex. CA, CS, etc.)</td><td>22</td><td>5.7</td></tr><tr><td rowspan="5">Occupation</td><td>Private Employee</td><td>181</td><td>46.9</td></tr><tr><td>Government Employee</td><td>129</td><td>33.4</td></tr><tr><td>Self-employed</td><td>65</td><td>16.8</td></tr><tr><td>Retired</td><td>11</td><td>2.8</td></tr><tr><td>Less than INR 750000</td><td>135</td><td>35.0</td></tr><tr><td rowspan="3">Income</td><td>INR 750001 to INR 1500000</td><td>181</td><td>46.9</td></tr><tr><td>Above INR 1500000</td><td>70</td><td>18.1</td></tr></tbody></table>

Note: 1 INR (Indian Rupees) = 0.012 USD (United States Dollar)

## Assessment of Measurement Model

The data analysis used the Partial Least Squares (PLS) method, which combines variance analysis and structural equation modeling (SEM). The analysis was conducted with SmartPLS 4 (Ringle et al., 2024). According to Hair et al. (2009), SEM is ideal for studies with multiple constructs and variables, enabling the simultaneous estimation of relationships. Additionally, PLS-SEM is more suitable for assessing the model's predictive capability compared to CB-SEM (Hair et al., 2016). To assess the measurement model, factor loadings and internal consistency reliability were first evaluated.

According to Hair et al. (2009), standardized factor loadings should ideally exceed 0.70, while a minimum acceptable threshold is 0.50, to ensure that constructs explain a significant proportion of the indicator's variance. In our study, all factor loadings ranged from 0.692 to 0.911, demonstrating satisfactory item reliability across constructs (see Table 2). For instance, items measuring green transparency exhibited loadings between 0.698 and 0.821, while items for repurchase intention ranged from 0.824 to 0.911, highlighting strong associations with their respective constructs.

Internal consistency reliability (refer Table 2) was assessed using Cronbach's α and Composite Reliability (rho_c). Following the recommendations of Nunnally (1978), Cronbach's α values above 0.70 are considered satisfactory, reflecting adequate internal consistency. In our study, Cronbach's α scores ranged from 0.726 to 0.805, confirming the reliability of the constructs. Furthermore, composite reliability values, as suggested by Fornell and Larcker (1981), should
exceed 0.70. The composite reliability for the constructs in this study ranged from 0.829 to 0.879, further corroborating robust internal consistency. These findings underline the measurement model's reliability and alignment with established thresholds.

Convergent validity was assessed using the Average Variance Extracted (AVE). Fornell and Larcker (1981) proposed that AVE values should exceed 0.50, indicating that the construct explains at least 50% of the variance in its indicators. The AVE values for all constructs in this study were above this threshold, ranging from 0.548 to 0.720, confirming acceptable convergent validity (refer Table 2). For example, the AVE for green transparency was 0.602, while for brand credibility, it was 0.720, demonstrating that these constructs adequately capture their associated items' variance.

Discriminant validity ensures that a construct measure is empirically unique and represents phenomena of interest that other measures in a structural equation model do not capture (Hair et al. 2009). The Heterotrait-Monotrait ratio (HTMT) was employed to evaluate the model's ability to distinguish between different variables. The results confirmed the model's discriminant validity, with all HTMT values falling within the acceptable limit of 0.85 (Henseler et al., 2015). This indicates that the model effectively differentiates between distinct traits without any overlap or confusion, ensuring a clear understanding of each variable (see Table 3).

<table><caption>Table 2. Measurement Model</caption><thead><tr><th>Constructs</th><th>Item Code</th><th>Factor Loadings</th><th>Cronbach's α</th><th>rho_c</th><th>AVE</th></tr></thead><tbody><tr><td rowspan="4">Green Transparency</td><td>GT1</td><td>0.769</td><td rowspan="4">0.781</td><td rowspan="4">0.858</td><td rowspan="4">0.602</td></tr><tr><td>GT2</td><td>0.810</td></tr><tr><td>GT3</td><td>0.698</td></tr><tr><td>GT4</td><td>0.821</td></tr><tr><td rowspan="4">Brand Image</td><td>BI1</td><td>0.806</td><td rowspan="4">0.726</td><td rowspan="4">0.829</td><td rowspan="4">0.548</td></tr><tr><td>BI2</td><td>0.730</td></tr><tr><td>BI3</td><td>0.717</td></tr><tr><td>BI4</td><td>0.704</td></tr><tr><td rowspan="3">Brand Credibility</td><td>BC1</td><td>0.858</td><td rowspan="3">0.805</td><td rowspan="3">0.885</td><td rowspan="3">0.720</td></tr><tr><td>BC2</td><td>0.841</td></tr><tr><td>BC3</td><td>0.846</td></tr><tr><td rowspan="4">Green Brand Loyalty</td><td>GBL1</td><td>0.692</td><td rowspan="4">0.795</td><td rowspan="4">0.868</td><td rowspan="4">0.623</td></tr><tr><td>GBL2</td><td>0.871</td></tr><tr><td>GBL3</td><td>0.753</td></tr><tr><td>GBL4</td><td>0.829</td></tr><tr><td rowspan="3">Repurchase Intention</td><td>RI1</td><td>0.824</td><td rowspan="3">0.793</td><td rowspan="3">0.879</td><td rowspan="3">0.709</td></tr><tr><td>RI2</td><td>0.911</td></tr><tr><td>RI3</td><td>0.831</td></tr></tbody></table>

<table><caption>Table 3. Discriminant Validity (HTMT Criterion)</caption><thead><tr><th>Constructs</th><th>Brand Credibility</th><th>Brand Image</th><th>Green Brand Loyalty</th><th>Green Transparency</th><th>Repurchase Intention</th></tr></thead><tbody><tr><td>Brand Credibility</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Brand Image</td><td>0.841</td><td></td><td></td><td></td><td></td></tr><tr><td>Green Brand Loyalty</td><td>0.742</td><td>0.682</td><td></td><td></td><td></td></tr><tr><td>Green Transparency</td><td>0.764</td><td>0.755</td><td>0.620</td><td></td><td></td></tr><tr><td>Repurchase Intention</td><td>0.425</td><td>0.365</td><td>0.732</td><td>0.347</td><td></td></tr></tbody></table>
Structural model assessment

The structural model results presented in Table 4 depict the path coefficients (β), standard deviations (SD), t statistics, and corresponding p-values for each hypothesis tested. Comparing these values across hypotheses reveals significant relationships. For instance, the results indicate a strong positive association between green transparency and brand image (H1, β = 0.590, t = 17.140, ρ = 0.000), implying statistical significance. Similarly, the relationship between green transparency and brand credibility is also supported (β = 0.618, t = 19.567, ρ = 0.000). Furthermore, the path from green transparency to green brand loyalty shows a significant positive association (β = 0.156, t = 2.777, ρ = 0.003). Additionally, brand image (β = 0.187, t = 3.200, ρ = 0.000) and brand credibility (β = 0.327, t = 6.021, ρ = 0.000) has a positive association to green brand loyalty. Finally, green brand loyalty exhibits a significant positive association to repurchase intention (β = 0.586, t = 11.783, ρ = 0.000). Figure 2 summarizes the results of the structural model analysis.

<table><caption>Table 4. Structural Model Results</caption><thead><tr><th>Hypotheses</th><th>β</th><th>S.D.</th><th>T</th><th>LLCI</th><th>ULCI</th><th>Results</th><th>R<sup>2</sup></th><th>f<sup>2</sup></th></tr></thead><tbody><tr><td>H<sub>1</sub> GT → BI</td><td>0.590</td><td>0.034</td><td>17.140*</td><td>0.526</td><td>0.641</td><td>Supported</td><td>0.348</td><td>0.534</td></tr><tr><td>H<sub>2</sub> GT → BC</td><td>0.618</td><td>0.032</td><td>19.567*</td><td>0.560</td><td>0.666</td><td>Supported</td><td>0.382</td><td>0.619</td></tr><tr><td>H<sub>3</sub> GT → GBL</td><td>0.156</td><td>0.056</td><td>2.777*</td><td>0.062</td><td>0.248</td><td>Supported</td><td>0.395</td><td>0.022</td></tr><tr><td>H<sub>4</sub> BI → GBL</td><td>0.187</td><td>0.059</td><td>3.200*</td><td>0.090</td><td>0.282</td><td>Supported</td><td>-</td><td>0.030</td></tr><tr><td>H<sub>5</sub> BC → GBL</td><td>0.372</td><td>0.062</td><td>6.021*</td><td>0.268</td><td>0.473</td><td>Supported</td><td>-</td><td>0.112</td></tr><tr><td>H<sub>6</sub> GBL → RI</td><td>0.586</td><td>0.050</td><td>11.783*</td><td>0.500</td><td>0.663</td><td>Supported</td><td>0.344</td><td>0.523</td></tr><tr><td>H<sub>7</sub> GT→BI→GBL</td><td>0.110</td><td>0.036</td><td>3.100*</td><td>0.042</td><td>0.181</td><td>Supported</td><td>-</td><td>-</td></tr><tr><td>H<sub>8</sub> GT→BC→GBL</td><td>0.230</td><td>0.041</td><td>5.548*</td><td>0.149</td><td>0.311</td><td>Supported</td><td>-</td><td>-</td></tr></tbody></table>

Note: * signify level of significance at 5% (p value < 0.05)

GT: Green Transparency, BI: Brand Image, BC: Brand Credibility, GBL: Green Brand Loyalty, RI: Repurchase Intention

Source: Derived from the SmartPLS 4 software and computed by authors

The coefficient of determination R² plays a crucial role in evaluating the overall model and testing hypotheses. It measures the combined influence of all independent variables on the dependent variable, serving as an indicator of in-sample predictive accuracy (Rigdon, 2012; Sarstedt et al., 2014). Based on Chin's (1998) widely accepted thresholds, R² values of 0.19, 0.33, and 0.67 are interpreted as weak, moderate, and substantial, respectively. This study adopts these benchmarks. As shown in Table 4, the observed R² values fall within the moderate range, suggesting that the proposed model meets the necessary standards for predictive accuracy.

To further analyze the model, the effect size f² of each independent variable must be assessed to determine its substantive impact on the dependent variable (Hair et al., 2016). Effect sizes are evaluated using two established guidelines. According to criteria by Cohen (1977), f² values of 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively, while values below 0.01 suggest no meaningful effect. In this study, guidelines by Cohen (1977) were applied. The f² effect sizes for green transparency on brand image (0.534), green transparency on brand credibility (0.619), and green brand loyalty on repurchase intention (0.523) demonstrated large effects. Meanwhile, the effect sizes for brand credibility on green brand loyalty (0.112), green transparency on green brand loyalty (0.022) and brand image on green brand loyalty (0.030) were categorized as small. These findings provide insight into the relative contributions of the independent variables to the model's outcomes.
<figure><img src="image_3.png" /><figcaption>Figure 2: Structural Model</figcaption></figure>

### Mediation analysis

The proposed mediating relationships were also examined (see Table 4). The results revealed that brand image partially mediated the relationship between green transparency and green brand loyalty [Indirect Effect = 0.110, 95% C.I (0.042; 0.181); p = 0.002 (H7)]. Furthermore, brand credibility partially mediated the relationship between green transparency and green brand loyalty [Indirect Effect = 0.230, 95% C.I (0.149; 0.311); p = 0.000 (H8)]. Overall, all mediation hypotheses were supported.

### PLSpredict analysis

The predictive accuracy of the proposed model was evaluated using the PLSpredict algorithm. To test the model's performance on new data, a data-splitting procedure was implemented, comparing outcomes on a validation holdout sample against those generated from the training sample. A 10-fold cross-validation method (Shmueli et al., 2019) was applied, dividing the dataset into ten equally sized subsets. During each iteration, the algorithm trained on nine subsets (k-1) and tested on the remaining one, designated as the holdout sample. This process was repeated across all ten subsets to ensure robust validation.

The results of the PLSpredict analysis (refer to Table 5) revealed that the errors of the PLS-SEM model (i.e. PLS_RMSE values for BC1, BI2, BI4, GBL2, RI1, RI2, and RI3, as well as the PLS_MAE values for BC2, BI4, GBL2, GBL3, RI1, RI2, and RI3) are lower as compared to their Linear Regression Model (LM) counterparts. The majority of the manifest variables had RMSE and MAE values smaller than those of the linear regression model. Also, all of the errors of the PLS-SEM model are lower than the Indicator Average (IA) values. Furthermore, the Q² values for all manifest variables brand credibility (0.377), brand image (0.342), green brand loyalty (0.242), and repurchase intention (0.075) were positive. These findings confirm the model's strong predictive power.

One of the variables i.e. repurchase intention had a low Q² value of 0.075, which suggests that the model has predictive relevance for repurchase intention but with limited strength compared to other constructs. This may stem from the complexity of the model leading to repurchase intention, which is influenced by various external and unmeasured factors outside the model's scope. Factors such as market competition, financial constraints, and changing preferences could impact repurchase decisions but were not directly considered in the study. Furthermore, the
modest Q² value implies that while the predictors of repurchase intention are significant, they explain only a small portion of its variance, highlighting the need for future research to include additional relevant factors for greater predictive accuracy.

<table><caption>Table 5. PLSpredict Analysis</caption><thead><tr><th rowspan="2">MV</th><th colspan="3">PLS-SEM predictive errors of the model</th><th colspan="2">LM predictive errors of the training sample naïve benchmark</th><th colspan="2">IA predictive errors</th><th colspan="3">LV (construct level) PLS-SEM prediction summary</th></tr><tr><th>Q² predict</th><th>PLS_R MSE</th><th>PLS_M AE</th><th>LM_R RMSE</th><th>LM_M AE</th><th>IA_R MSE</th><th>IA_M AE</th><th>Q² predict</th><th>RMSE</th><th>MAE</th></tr></thead><tbody><tr><td>BC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.377</td><td>0.794</td><td>0.605</td></tr><tr><td>BC1</td><td>0.234</td><td>1.290</td><td>1.008</td><td>1.278</td><td>0.990</td><td>1.474</td><td>1.191</td><td></td><td></td><td></td></tr><tr><td>BC2</td><td>0.327</td><td>1.218</td><td>0.955</td><td>1.228</td><td>0.960</td><td>1.485</td><td>1.224</td><td></td><td></td><td></td></tr><tr><td>BC3</td><td>0.251</td><td>1.215</td><td>0.936</td><td>1.212</td><td>0.933</td><td>1.404</td><td>1.122</td><td></td><td></td><td></td></tr><tr><td>BI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.342</td><td>0.817</td><td>0.642</td></tr><tr><td>BI1</td><td>0.290</td><td>1.171</td><td>0.909</td><td>1.169</td><td>0.896</td><td>1.389</td><td>1.150</td><td></td><td></td><td></td></tr><tr><td>BI2</td><td>0.159</td><td>1.233</td><td>0.978</td><td>1.240</td><td>0.974</td><td>1.345</td><td>1.040</td><td></td><td></td><td></td></tr><tr><td>BI3</td><td>0.153</td><td>1.295</td><td>1.042</td><td>1.294</td><td>1.041</td><td>1.407</td><td>1.101</td><td></td><td></td><td></td></tr><tr><td>BI4</td><td>0.134</td><td>1.318</td><td>1.072</td><td>1.328</td><td>1.082</td><td>1.417</td><td>1.130</td><td></td><td></td><td></td></tr><tr><td>GBL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.242</td><td>0.878</td><td>0.663</td></tr><tr><td>GBL1</td><td>0.257</td><td>1.268</td><td>1.029</td><td>1.225</td><td>0.965</td><td>1.471</td><td>1.201</td><td></td><td></td><td></td></tr><tr><td>GBL2</td><td>0.128</td><td>1.580</td><td>1.217</td><td>1.581</td><td>1.230</td><td>1.692</td><td>1.365</td><td></td><td></td><td></td></tr><tr><td>GBL3</td><td>0.038</td><td>1.577</td><td>1.209</td><td>1.576</td><td>1.221</td><td>1.608</td><td>1.282</td><td></td><td></td><td></td></tr><tr><td>GBL4</td><td>0.164</td><td>1.372</td><td>1.047</td><td>1.368</td><td>1.040</td><td>1.500</td><td>1.176</td><td></td><td></td><td></td></tr><tr><td>RI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.075</td><td>0.969</td><td>0.751</td></tr><tr><td>RI1</td><td>0.068</td><td>1.443</td><td>1.148</td><td>1.454</td><td>1.155</td><td>1.495</td><td>1.160</td><td></td><td></td><td></td></tr><tr><td>RI2</td><td>0.066</td><td>1.705</td><td>1.360</td><td>1.715</td><td>1.360</td><td>1.764</td><td>1.453</td><td></td><td></td><td></td></tr><tr><td>RI3</td><td>0.024</td><td>1.671</td><td>1.313</td><td>1.683</td><td>1.335</td><td>1.691</td><td>1.366</td><td></td><td></td><td></td></tr></tbody></table>

BC: Brand Credibility, BI: Brand Image, GBL: Green Brand Loyalty, RI: Repurchase Intention, MV: Manifest Variable, LM: Linear Regression Model, LV: Latent Variable, IA: Indicator Average

Source: Derived from the SmartPLS 4 software and computed by authors

Further, we completed the evaluation by assessing the predictive power of the model with the PLSpredict algorithm (Sharma et al., 2021) and Cross-Validated Predictive Ability Test (CVPAT) (Liengaard et al., 2021). Analysis of both the target construct Repurchase Intention and the overall model revealed predictive validity. For both the Repurchase Intention and the overall model, the PLS-SEM predictions showed a significantly lower average loss than the naïve indicator-average prediction benchmark (see Table 6). Since all PLS-SEM predictions are significantly better than the naïve indicator average (IA) prediction benchmark, we conclude that Model has good predictive validity.

<table><caption>Table 6. CVPAT Summary of PLS-SEM vs. Indicator Average (IA)</caption><thead><tr><th></th><th>PLS loss</th><th>IA loss</th><th>Average loss difference</th><th>t value</th><th>p value</th></tr></thead><tbody><tr><td>Brand Credibility</td><td>1.542</td><td>2.117</td><td>-0.575</td><td>7.519</td><td>0.000</td></tr><tr><td>Brand Image</td><td>1.577</td><td>1.931</td><td>-0.355</td><td>5.821</td><td>0.000</td></tr><tr><td>Green Brand Loyalty</td><td>2.118</td><td>2.466</td><td>-0.348</td><td>4.712</td><td>0.000</td></tr><tr><td>Repurchase Intention</td><td>2.593</td><td>2.735</td><td>-0.142</td><td>2.593</td><td>0.010</td></tr><tr><td>Overall</td><td>1.942</td><td>2.296</td><td>-0.354</td><td>6.649</td><td>0.000</td></tr></tbody></table>

Source: Derived from the SmartPLS 4 software and computed by authors
DISCUSSION AND IMPLICATIONS

The relationship between green transparency and brand image was found to be significant, providing robust support for the hypothesis (H₁). This result underscores the role of transparency in fostering an environmentally responsible brand image. In the context of electric vehicle industry, where sustainability practices must be explicit and verifiable, such transparency is indispensable. The positive impact of green transparency on brand image aligns with previous research done by Deng and Yan (2022), who emphasized that transparency enhances brand perception by building consumer trust and reinforcing environmental accountability. This highlights how green transparency serves as a strategic driver for cultivating a positive and credible brand image, particularly in sectors where ecological integrity is a key consumer expectation.

The relationship between green transparency and brand credibility was found to be significant, confirming the associated hypothesis (H₂). This result demonstrates that transparency plays a vital role in mitigating consumer skepticism, enhancing the credibility of environmental claims. The positive impact of green transparency on brand credibility aligns with previous research done by Sansome et al. (2024), which argued that transparent practices contribute to the perception of authenticity and reliability in brand communications. This relationship is particularly relevant in the electric vehicle sector, where claims of sustainability and eco-friendliness are frequently scrutinized by environmentally conscious consumers. By reducing skepticism and bolstering credibility, green transparency provides a foundation for fostering long-term consumer trust.

The study found a significant relationship between green transparency and green brand loyalty, supporting the corresponding hypothesis (H₃). This finding reinforces the notion that transparency cultivates loyalty by aligning brand values with those of eco-conscious consumers. The positive impact of green transparency on green brand loyalty aligns with previous research done by Tiwari (2022), who demonstrated that brands committed to transparent and sustainable practices are more likely to engender loyalty among consumers who prioritize environmental responsibility. This insight underscores the strategic importance of green transparency as a mechanism for fostering strong and enduring consumer relationships.

The relationship between brand image and green brand loyalty was also found to be significant, providing additional support for the hypothesis (H₄). The positive impact of brand image on green brand loyalty aligns with previous research done by Phan et al. (2024). This brand image not only strengthens loyalty but also enhances the brand's ability to differentiate itself in a competitive marketplace. In the electric vehicle sector, where consumers are deeply invested in sustainability and environmental stewardship, a strong brand image will serve as a critical determinant of loyalty.

The relationship between brand credibility and green brand loyalty was found to be significant, further validating the hypothesis (H₅). The positive impact of brand credibility on green brand loyalty aligns with previous research done by Fatma and Khan (2023). Brand credibility enhances trust in a brand's environmental claims and strengthens consumer confidence, as noted by Fatma and Khan (2023). In the electric vehicle industry where consumers demand transparency and authenticity, credible brands are better positioned to establish loyalty.

The study confirmed a significant relationship between green brand loyalty and repurchase intention, supporting the hypothesis (H₆). This result underscores the critical role of loyalty in driving repeat purchase behavior. The positive impact of brand credibility on green brand loyalty aligns with previous research done by Pancic et al. (2023), who emphasized the importance of
loyalty in fostering long-term consumer engagement and retention. In the context of the electric vehicle industry, where purchasing decisions are often high-involvement and value-driven, this relationship is particularly noteworthy.

The partial mediations of brand image and brand credibility between green transparency and green brand loyalty were found significant, which supports both of the hypotheses ($H_7$ and $H_8$). These findings shed light on the cognitive and emotional mechanisms that link transparency to loyalty. Brand image serves as both an emotional and cognitive bridge, transforming transparent practices into positive brand associations, as noted by Watson et al. (2024). The significant partial mediation of brand image between green transparency and green brand loyalty aligns with previous research done by Watson et al. (2024). By creating a favorable perception of the brand, brand image fosters consumer attachment and strengthens loyalty. The partial mediation of brand image in this relationship highlights its critical role in translating transparency into meaningful consumer outcomes. Brand credibility, on the other hand, provides a foundation of trust that enhances the effectiveness of transparency in fostering loyalty. As emphasized by Arham and Dwita (2021), credible brands are better equipped to convert consumer trust into long-term commitment. The significant partial mediation of brand image between green transparency and green brand loyalty aligns with previous research done by Arham and Dwita (2021). These findings highlight the complementary roles of brand image and brand credibility in driving loyalty, particularly in high-involvement sectors like electric vehicles, where consumers demand both emotional resonance and factual authenticity.

## Theoretical Implications

This research highlights the dual role of green transparency as both a stimulus influencing brand image and brand credibility, which then drives green brand loyalty and, ultimately, repurchase intention. Unlike the traditional SOR model, which lacks behavioral outcomes, this study emphasizes the consequences of green transparency, showing how it fosters lasting consumer engagement through brand image and brand credibility (Deng & Yang, 2022; Sansome et al., 2024). By incorporating brand image and brand credibility as mediators, it refines the SOBC model, revealing how cognitive (brand image) and emotional (brand credibility) evaluations turn transparency into loyalty and repurchase behaviors (Fatma & Khan, 2023). These findings enrich the literature by integrating trust (credibility) and emotional resonance (image), advancing understanding of how environmental transparency shapes consumer behavior in high-involvement markets like electric vehicles (Rahman & Nguyen-Viet, 2023). Additionally, this study's comparison to previous SOBC applications highlights its contribution in demonstrating how green transparency influences perceptions and behaviors in contexts marked by skepticism and information asymmetry (Chakraborty et al., 2022; Laato et al., 2020). Future research could extend this framework to other green products, exploring further mediators or moderators such as environmental consciousness (Konuk et al., 2023). These insights bridge theoretical gaps and refine the SOBC model, stressing the role of transparency in fostering responsible consumer behavior in emerging markets (Dhir et al., 2021b).

## Practical Implications

Green transparency positively influences brand image and brand credibility, which fosters loyalty and encourage repurchase intention among customers of electric vehicles. Electric vehicle companies should prioritize transparent communication through detailed environmental reports and third-party certifications (Deng & Yang, 2022; Lee & Chen, 2019). Such initiatives not only strengthen the brand's reputation but also help differentiate it in a competitive market. Policymakers should mandate standardized environmental disclosures and clear labeling, enabling consumers to make informed decisions based on verified sustainability claims (Rahman
& Nguyen-Viet, 2023).

Green transparency builds brand credibility by addressing consumer skepticism through evidence-based claims, such as lifecycle analyses and transparent renewable energy usage in production (Mim et al., 2022). To enhance credibility, electric vehicles companies should proactively communicate their sustainability efforts and align them with verifiable data. Policymakers can support this by introducing stricter regulations on greenwashing and promoting accountability mechanisms, reinforcing consumer trust in green marketing claims (Guèvremont, 2019; Thøgersen, 2021).

Green transparency acts as a driver of green brand loyalty by meeting expectations for authenticity and shared values. Electric vehicles brands should employ emotional storytelling that resonates with consumers' environmental concerns, creating a deeper connection with the brand (Watson et al., 2024). Partnering with trusted environmental organizations can further enhance credibility and loyalty (Arham & Dwita, 2021). Integrating transparency into broader sustainability goals ensures consistency, which strengthens long-term consumer allegiance (Lee et al., 2023).

A strong brand image rooted in green transparency fosters emotional connections, which play a crucial role in driving loyalty. Electric vehicles companies should focus on creating campaigns that highlight their alignment with consumer values and environmental goals, showcasing their role as sustainability leaders. Leveraging visual storytelling and impactful narratives further reinforces these connections, making it more likely for consumers to remain loyal to the brand (García-Salirrosas et al., 2024).

Credibility is a cornerstone of loyalty, especially in high-scrutiny sectors like electric vehicles, where environmental claims are often closely examined (Mansoor & Paul, 2021). Research indicates that transparent and credible communication reduces skepticism, fosters trust, and strengthens the relationship between consumers and brands (Adnan et al., 2019). Electric vehicles companies should address consumer doubts by adhering to transparency and consistently delivering on their promises. Establishing partnerships with independent environmental organizations can further validate their sustainability claims, reinforcing consumer confidence and encouraging long-term loyalty (Mansoor et al., 2022).

Green brand loyalty significantly impacts repurchase intention, highlighting the importance of retaining loyal customers for sustained market growth (Pancic et al., 2023). Electric vehicles companies should create loyalty programs that reward repeat purchases, such as discounts, exclusive charging access, or eco-rewards programs (Jaiswal & Kant, 2018). Policymakers can amplify these efforts by providing subsidies or tax incentives for loyal Electric Vehicles users, further incentivizing sustainable consumption (Goel et al., 2021).

The roles of brand image and brand credibility between green transparency and green brand loyalty suggest strategies for fostering consumer loyalty. Marketing should use emotional storytelling aligned with consumers' environmental values (Watson et al., 2024), while building credibility through partnerships with trusted environmental organizations (Arham & Dwita, 2021). Integrating these into broader sustainability goals can enhance a consistent brand narrative (Lee et al., 2023).

## LIMITATIONS AND FUTURE RECOMMENDATIONS

This study contributes to understanding green transparency, brand image, brand credibility, green brand loyalty, and repurchase intention in the context of electric vehicles, but has some
limitations. First, its cross-sectional design limits causal inferences. Future research could use
longitudinal designs to capture evolving relationships among these constructs. Second, the
sample from Maharashtra, India, may limit generalizability, as consumer perceptions vary across
cultures and regions. Future studies should explore diverse contexts to improve external validity.
The use of non-probability purposive sampling could introduce bias, so employing random or
stratified sampling could enhance representativeness. Incorporating multi-method approaches,
such as combining self-reports with behavioral data, would improve measurement reliability.
While the SOBC framework was effective, other theories, like TPB or VBN, could offer deeper
insights into sustainability-related consumer behavior. Future research could also explore the
roles of variables like environmental consciousness or regulatory influences to better understand
how green transparency affects consumer behavior. Expanding this work to other green products,
like renewable energy or eco-friendly apparel, could confirm the findings. Experimental studies
might help uncover the causal mechanisms between green transparency, brand image, brand
credibility, and green brand loyalty, offering clearer insights into decision-making processes.
Practical implications also warrant further investigation. This study highlights the importance
of green transparency in building brand image and brand credibility, but future research should
examine the specific attributes of green transparency (e.g., clarity, frequency, communication
channels) that most effectively drive these outcomes. Understanding the financial impact of green
brand loyalty on green markets could also provide valuable insights for businesses. By addressing
these limitations, future studies could deepen both theoretical and practical understanding role
of green transparency in shaping consumer behavior, advancing sustainable marketing and
engagement.

CONCLUSION

This study contributes to the growing field of sustainable consumer behavior by using the SOBC
framework to explore how green transparency affects green brand loyalty and repurchase
intention in the electric vehicles market. It identifies brand image and brand credibility as key
mediators, enhancing understanding of the cognitive and emotional processes that drive
consumer loyalty. The findings show that green transparency positively influences brand image
and brand credibility, which then impact green brand loyalty and, ultimately, repurchase
intention. These results highlight the importance of transparent communication in building trust,
aligning brands with consumer values, and fostering loyalty in high-involvement green product
markets. This study refines the SOBC framework by incorporating the downstream effects of
consumer behavior, such as repurchase intention. By showing the mediating roles of brand image
and brand credibility, it bridges gaps in sustainable branding literature, providing empirical
evidence on how transparency influences both cognitive evaluations and emotional connections.
The research validates the SOBC framework in a new context and expands its relevance to
sustainability-driven markets. However, the study's limitations, such as its cross-sectional design,
geographically limited sample, and reliance on self-reported data, suggest the need for
longitudinal, cross-cultural, and multi-method research to strengthen generalizability. Despite
these constraints, the research offers valuable insights for academics and practitioners,
emphasizing the importance of green transparency in shaping sustainable consumer behavior.
For businesses, it underscores the need for transparent communication of environmental
initiatives to build trust, align with consumer expectations, and strengthen brand image and
brand credibility. Moreover, prioritizing green transparency can offer a competitive edge and
drive long-term consumer engagement in the environmentally conscious electric vehicles market.
This study provides a foundational understanding of the relationships between green
transparency, brand image, brand credibility, green brand loyalty, and repurchase intention, and
opens new research avenues. By combining cognitive, emotional, and behavioral perspectives, the
findings offer both theoretical advancements and practical insights for advancing sustainable
marketing strategies.

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*Disclaimer: The opinions, findings, and conclusions presented in this manuscript are solely those of the authors and do not necessarily represent the views of JASEM or its editorial team. JASEM and its editorial team accept no responsibility or liability for the accuracy, completeness, or reliability of the content.*
# APPENDIX 1: QUESTIONNAIRE ITEMS

## *Green Transparency (Deng & Yang, 2022)*

* The brand provides clear green element information (GT1)

* This brand explains clearly how it controls the emissions caused by its production processes that could harm the environment (GT2)

* This brand provides relevant information regarding environmental issues associated with its production processes (GT3)

* The environmental policies and practices of this brand are provided to customers in a clear and complete way (GT4)

## *Brand Image (Foroudi et al., 2018)*

* I like the brand compared to other companies in the same sector (BI1)

* I think other consumers like the company as well (BI2)

* The company's logo communicates about the company to its customers (BI3)

* The image of company is consistent with how I like to see myself (BI4)

## *Brand Credibility (Sekhar et al., 2022)*

* This brand delivers what it promises (BC1)

* This brand's product claims are believable (BC2)

* This brand has a name you can trust (BC3)

## *Green Brand Loyalty (Chen, 2013)*

* I am willing to repurchase this product because of its (environmental) functions (GBL1)

* I prefer purchasing this product to other products because of its (environmental) performance (GBL2)

* I seldom consider switching to other products because of this product's (environmental) concern (GBL3)

* I intend to continue buying this product because it is (environmentally) friendly (GBL4)

## *Repurchase Intention (Sehgal et al., 2023)*

* I will repurchase this green brand products again in the future (RI1)

* If I could have done it again, I would have chosen this green brand (RI2)

* I plan to purchase this green brand product (RI3)