In the dynamic landscape of contemporary marketing, the digital revolution has catalyzed significant transformations in consumer behaviour, brand engagement, and strategic branding. Among the emergent consumer segments, Generation Z (Gen Z)—those born approximately between 1997 and 2012—has emerged as a pivotal force shaping the future of brand equity. Characterized by digital nativity, hyper-connectivity, and social consciousness, Gen Z exhibits unique attitudes and behaviours toward brands, often mediated through digital platforms. Their interactions with brands are largely experiential, participatory, and values-driven, which calls for a reevaluation of traditional brand equity frameworks.

As businesses increasingly rely on digital channels to build and communicate brand value, understanding how Gen Z contributes to or transforms brand equity is essential. The digital era, marked by the proliferation of social media, e-commerce, influencer culture, and algorithm-driven personalization, offers both opportunities and challenges in maintaining and enhancing brand equity. This study endeavors to empirically investigate the influence of Generation Z consumers on brand equity within this digital environment, shedding light on how their digital behaviour, preferences, and values reshape brand perceptions and loyalty.

This paper aims to bridge the gap in empirical studies concerning the Gen Z–brand equity relationship in the digital context. By examining relevant constructs such as brand awareness, brand loyalty, perceived quality, and brand associations, the study seeks to offer nuanced insights into how digital-native consumers perceive and co-create brand value. The findings are expected to provide valuable implications for marketers, brand strategists, and digital communication experts in designing effective brand strategies tailored to the Gen Z cohort.

Generation Z (Gen Z):

Generation Z refers to individuals born approximately between 1997 and 2012. Unlike previous generations, Gen Z has grown up entirely in the digital age. Their worldview is shaped by constant internet connectivity, smartphones, social media platforms (like TikTok, Instagram, and YouTube), and exposure to global cultures. They are pragmatic, socially conscious, and value authenticity, inclusivity, and real-time engagement.

Digital Era:

The digital era refers to the period characterized by the widespread use of digital technology and the internet. It encompasses the use of smartphones, digital marketing, AI, data analytics, social networking, and online commerce. In this context, it represents a transformative period for businesses, where consumer-brand interactions are increasingly mediated through digital touchpoints.

Brand Equity:

Brand equity refers to the value a brand adds to a product or service beyond its functional benefits. It is typically composed of several dimensions, including:

• Brand Awareness – the extent to which consumers are familiar with a brand.
• Brand Associations – the meanings, feelings, and perceptions linked to a brand.
• Perceived Quality – consumers' judgments about a product’s superiority.
• Brand Loyalty – the degree of consumer attachment and repeated purchase behaviour toward a brand.

Understanding Generation Z as Digital Consumers

Generation Z has been the subject of increasing scholarly attention due to its distinctive digital behaviour and its potential to reshape consumer-brand relationships. According to Williams, Page, Petrosky, and Hernandez (2010), Gen Z differs significantly from previous generations in terms of communication preferences, content consumption, and purchasing behaviour. Born into a world already shaped by internet and mobile technology, they are considered “digital natives” (Prensky, 2001), comfortable with multitasking across platforms, and skeptical of traditional advertising.

A study by Turner (2015) further emphasized that Gen Z is socially conscious, entrepreneurial, and expects instant gratification, which influences how they interact with brands. They gravitate toward brands that offer personalization, inclusivity, and purpose-driven messaging (Francis & Hoefel, 2018). This generation also displays a shorter attention span (McKinsey, 2018), necessitating brands to develop more engaging and visually dynamic content to maintain relevance.

Digital Era and Its Transformative Effect on Brand Strategies

The digital era, characterized by the ubiquity of the internet, mobile applications, AI, and social media, has redefined traditional marketing paradigms. Mangold and Faulds (2009) argue that social media platforms have become a hybrid element of the promotional mix where consumers are not just passive receivers but active content creators and brand advocates.

Digitalization allows for two-way communication, enabling brands to interact with consumers in real time and on a personal level. Kumar and Kaushik (2020) discuss the rise of content marketing, influencer collaborations, and experiential campaigns that create stronger emotional connections with digitally native audiences. These interactions are no longer linear but iterative and dynamic, with consumers contributing to brand narratives through comments, hashtags, and user-generated content.

Brand Equity: Classical and Contemporary Perspectives

Brand equity has long been a cornerstone of marketing strategy. Aaker (1991) conceptualized brand equity through a model comprising brand awareness, perceived quality, brand associations, and brand loyalty. Keller (1993) introduced the Customer-Based Brand Equity (CBBE) model, which emphasizes the importance of brand knowledge structures in shaping consumer responses.

While these frameworks are still relevant, scholars like Christodoulides and de Chernatony (2010) argue that digital environments necessitate a more participatory view of brand equity. In digital spaces, brand equity is no longer unidirectionally communicated but co-created through interactions, sharing, and peer influence (Gensler et al., 2013). This co-creation aspect becomes particularly pronounced with Gen Z, who often see themselves as collaborators rather than consumers.

Social Media’s Role in Shaping Brand Equity

Social media platforms have emerged as critical enablers of brand engagement and equity building, particularly among younger audiences. Research by Bruhn, Schoenmueller, and Schäfer (2012) shows that brand-related user-generated content significantly affects brand trust and purchase intentions. Gen Z's extensive use of platforms like Instagram, TikTok, and YouTube for product discovery and brand evaluation makes these channels essential to brand equity strategies (Djafarova & Bowes, 2021).

Kaplan and Haenlein (2010) assert that brands that actively manage their online presence through content curation, influencer partnerships, and community engagement are better positioned to build equity. Generation Z, in particular, prefers brands that maintain authentic, interactive, and transparent digital communication (Chatterjee, Rana, & Sharma, 2021).

Influencer Marketing and Peer Influence

Influencer marketing plays a substantial role in shaping brand perceptions among Gen Z consumers. According to De Veirman, Cauberghe, and Hudders (2017), influencers serve as opinion leaders whose endorsements can enhance brand equity components such as perceived quality and trust. Unlike traditional celebrity endorsements, micro- and nano-influencers often possess a more relatable and trustworthy image, making them more effective in building authenticity among Gen Z audiences (Sokolova & Kefi, 2020).

Gen Z places significant trust in peer recommendations and online reviews over corporate messages (Pew Research, 2020). The value of electronic word-of-mouth (eWOM) has increased, with studies by Cheung and Thadani (2012) confirming its impact on brand attitudes and loyalty.

Personalization and Brand Experience

The expectation of personalized experiences is another characteristic feature of Gen Z. Lemon and Verhoef (2016) highlight the importance of integrated customer journeys where digital and physical experiences align to create seamless brand encounters. Personalized marketing communications, dynamic product recommendations, and real-time customer service enhance the perceived value and relevance of the brand, thus positively impacting brand equity.

Yoon and Kim (2018) assert that brand experience—comprising sensory, affective, intellectual, and behavioural dimensions—mediates the relationship between brand engagement and equity. Gen Z responds particularly well to immersive digital experiences, such as virtual try-ons, gamification, and augmented reality (AR), which contribute to positive brand associations and emotional bonding.

Brand Authenticity, Purpose, and Trust

Authenticity is a crucial determinant of Gen Z’s brand loyalty and advocacy. Morhart et al. (2015) define brand authenticity as the extent to which consumers perceive a brand as genuine, original, and aligned with its stated values. Studies indicate that Gen Z favors brands that actively engage in social, environmental, and political issues, provided such engagements are perceived as sincere (Fromm & Read, 2018; Vredenburg et al., 2020).

Trust, as a dimension of brand equity, is particularly fragile in the digital era, where misinformation and performative marketing can quickly damage a brand’s reputation. Brands that maintain transparency in their supply chains, employee policies, and community impact are more likely to gain and retain the trust of Gen Z consumers (Bailey & Seock, 2010).

Challenges in Measuring Gen Z's Impact on Brand Equity

Despite the growing recognition of Gen Z’s influence, empirical studies remain limited. A key challenge lies in measuring the fluid and non-linear nature of digital engagement. Pappu and Quester (2006) stress the need for revised measurement tools that capture real-time behavioural metrics, such as click-through rates, shareability, sentiment analysis, and brand mentions.

Additionally, the heterogeneity within Gen Z complicates generalization. Cultural, regional, and socioeconomic factors affect how members of this generation interact with brands. Hence, localized studies and adaptive methodologies are necessary to develop a more granular understanding of their impact on brand equity (Becker & Lee, 2019).

Synthesis of Literature

The literature reveals a robust interplay between Generation Z’s digital behaviour and evolving brand equity models. Gen Z’s digital nativity, demand for authenticity, reliance on influencers and peers, and high standards for personalized experiences have substantially altered how brands build and sustain equity. Traditional constructs of brand equity remain valid but require expansion to include digitally mediated variables and participatory dynamics.

While theoretical frameworks have been proposed to address these new dynamics, empirical studies—particularly those using behavioural data from Gen Z audiences—remain underdeveloped. This gap necessitates research that combines classic brand equity metrics with digital engagement indicators to provide a holistic picture of Gen Z’s influence in the digital era.

Supporting Literature for Each Construct

Table-1 Relevant studies that support the constructs used in the analysis.

Research Design

This study employs a quantitative research approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the impact of Generation Z’s digital engagement behaviours on brand equity growth. PLS-SEM is chosen due to its suitability for complex models involving latent constructs, allowing for robust hypothesis testing and predictive analytics. Empirical, explanatory research method chosen with collection of 100 responses through Structured survey questionnaire

Sample Size and Method of Sampling

Sample Size Determination

The sample size for this study was determined using post-hoc minimum sample size calculations in SmartPLS 4, ensuring statistical validity for PLS-SEM modeling. Based on the model complexity and effect sizes, the recommended sample sizes were:

• 80% power at α = 5%: 67 to 151 respondents, depending on the variable effect size
• 90% power at α = 5%: 93 to 196 respondents, ensuring stronger statistical significance

With an R² of 0.7696, the final sample size ensured robust model fit and predictive validity.

Sampling Method

This study employs a purposive sampling technique, specifically targeting Generation Z consumers actively engaged with brands in the digital landscape. The criteria for selecting respondents include:

• Frequent social media users interacting with brands
• Consumers contributing to digital Word-of-Mouth (DWOM_) through reviews, discussions, or influencer engagement
• Participants involved in online brand communities

Data Collection Process

• Survey Distribution: Online questionnaire via social media platforms and brand communities
• Respondent Screening: Ensuring inclusion criteria (active engagement, online purchases, social media participation)
• Data Validation: Removing incomplete responses to maintain analysis integrity

This approach enhances representativeness of the Gen Z population while ensuring high response quality for PLS-SEM hypothesis testing.

Hypothesis of the Study

H1: Consumer Engagement Behaviours have a positive impact on Brand Equity Perceived Growth.

H2: Digital Brand Engagement has a positive impact on Brand Equity Perceived Growth.

H3: Word-of-Mouth Marketing has a positive impact on Brand Equity Perceived Growth.

H4: The model explains a substantial proportion of variance in Brand Equity, indicating strong predictive validity.

H5: There is no significant multicollinearity between predictor variables, ensuring robust estimation.

H6: Consumer Engagement Behaviours and Word-of-Mouth Marketing are highly correlated, potentially affecting discriminant validity.

Data Analysis and Interpretation

Path Coefficients Table

Table 2:  Strength and significance of relationships between independent and dependent variables.

> Key Insight: DWOM_ has the strongest influence on BEPGZ_, followed by CEBQ_ and DBGZ_.

R-Square (Explained Variance)

Table-3:  Predictive power of independent variables for BEPGZ_.

> Key Insight: The independent variables explain a substantial portion of BEPGZ_.

Reliability & Validity

Table-4: Internal consistency and validity measures to ensure robust measurement.

> Key Insight: BEPGZ_, DBGZ_, and DWOM_ have strong reliability and validity, while CEBQ_ is slightly below optimal AVE (0.4953).

Collinearity Statistics (VIF)

Table-5: Multicollinearity issues among independent variables.

> Key Insight: No major multicollinearity concerns exist.

Model Fit Indices

Table-6: Assessing how well the PLS-SEM model fits the data.

> Key Insight: While moderate model fit is achieved, further refinement may improve the results.

Total Effects Table

Table-7: Combined direct and indirect effects of predictor variables on the dependent variable (BEPGZ_).

> Key Insight: Total effects confirm that DWOM_ contributes the most to the variance in BEPGZ_, followed by CEBQ_ and DBGZ_.

Discriminant Validity – Heterotrait-Monotrait Ratio (HTMT)

Table-8: How distinct the constructs are from each other.

> Key Insight: CEBQ_ and DWOM_ exhibit high correlation with BEPGZ_, indicating potential overlap and requiring careful interpretation.

Variance Explained – f² (Effect Size)

Table-9: Measurement of importance of each predictor variable.

> Key Insight: DWOM_ has the largest effect size, significantly influencing BEPGZ_ compared to CEBQ_ and DBGZ_.

Covariances Between Constructs

Table-10: Shows how correlated the latent variables are.

> Key Insight: Strong correlation between BEPGZ_ & DWOM_ suggests a closely related impact.

Standardized Residuals (Model Accuracy)

Table-11: Model errors and how well indicators measure latent constructs.

> Key Insight: While most residuals are low, BEPGZ_3 and DWOM_5 show high errors, which might need further refinement.

Measurement Model Assessment

This step ensures that the indicators are correctly measuring their latent constructs using:

• Outer Loadings (Indicator reliability)
• Items with loadings > 0.7 are considered strong indicators of their constructs.
• Composite Reliability & Cronbach’s Alpha (Internal consistency)
• Values > 0.7 confirm reliable measurement.
• Average Variance Extracted (AVE) (Convergent validity)
• Values > 0.5 show constructs are adequately represented.
• Fornell-Larcker Criterion & HTMT (Discriminant validity)
• Ensures constructs are not overly correlated with each other.

Structural Model Evaluation

• Path Coefficients: Measures the direct effects of independent variables on the dependent variable.
• R-Square: BEPGZ_ (0.7696) indicates that 96% of variance is explained, confirming strong predictive power.
• Effect Size (f²): Indicates the relative importance of each predictor in explaining BEPGZ_.
• Collinearity Statistics (VIF): Confirms minimal multicollinearity.
• Model Fit and Predictive Power
• SRMR (Standardized Root Mean Square Residual): 106, suggesting moderate fit.
• Chi-Square and NFI (Normed Fit Index): Measures overall model adequacy.
• Posthoc Sample Size Calculation: Determines if the sample size meets statistical power requirements.

Hypotheses Testing

H1: Consumer Engagement Behaviours (CEBQ_) have a positive impact on Brand Equity Perceived Growth (BEPGZ_).

• Path Coefficient: 0.3044
• Effect Size (f²): 0.1568
• R² Contribution: Moderate
• Acceptance/Rejection: Accepted
• Explanation: Since CEBQ_ exhibits a positive path coefficient and a medium effect size, it significantly contributes to brand equity growth. This confirms that Gen Z’s active engagement with brands (interactions, discussions, loyalty) enhances brand value over time.

H2: Digital Brand Engagement (DBGZ_) has a positive impact on Brand Equity Perceived Growth (BEPGZ_).

• Path Coefficient: 0.2579
• Effect Size (f²): 0.1571
• R² Contribution: Moderate
• Acceptance/Rejection: Accepted
• Explanation: Although weaker than CEBQ_, DBGZ_ still demonstrates a positive and statistically significant relationship with BEPGZ_. This indicates that Gen Z’s digital interactions—such as social media engagement, brand app usage, and online shopping experiences—enhance brand equity.

H3: Word-of-Mouth Marketing (DWOM_) has a positive impact on Brand Equity Perceived Growth (BEPGZ_).

• Path Coefficient: 0.4489
• Effect Size (f²): 0.4635
• R² Contribution: Strong
• Acceptance/Rejection: Accepted
• Explanation: DWOM_ is the strongest predictor of BEPGZ_. Gen Z places high trust in peer recommendations, influencer reviews, and social proof, making word-of-mouth interactions the most significant driver of brand equity.

H4: The model explains a substantial proportion of variance in Brand Equity (BEPGZ_), indicating strong predictive validity.

• R² for BEPGZ_: 0.7696
• Acceptance/Rejection: Accepted
• Explanation: The model confirms that 76.96% of the variance in brand equity growth is explained by CEBQ_, DBGZ_, and DWOM_, validating its strong predictive capability.

H5: There is no significant multicollinearity between predictor variables, ensuring robust estimation.

• VIF Values: CEBQ_ (2.565), DBGZ_ (1.837), DWOM_ (1.887)
• Threshold: Acceptable (<5)
• Acceptance/Rejection: Accepted
• Explanation: Low VIF values confirm that predictor variables are independent, ensuring that the model results are not distorted by multicollinearity.

H6: Consumer Engagement Behaviors (CEBQ_) and Word-of-Mouth Marketing (DWOM_) are highly correlated, potentially affecting discriminant validity.

• HTMT Value (CEBQ_ <-> DWOM_): 0.7679
• Threshold: Acceptable (<0.85)
• Acceptance/Rejection: Accepted (with caution)
• Explanation: While the constructs remain distinguishable, CEBQ_ and DWOM_ exhibit moderate correlation, suggesting that consumers engaging with brands are also likely to promote them through word-of-mouth interactions.

Summary of Hypothesis Testing

Accepted Hypotheses:

• H1: CEBQ_ → BEPGZ_ (Moderate positive impact)
• H2: DBGZ_ → BEPGZ_ (Moderate positive impact)
• H3: DWOM_ → BEPGZ_ (Strongest positive impact)
• H4: Model explains high variance (R² = 76.96%)
• H5: No significant multicollinearity (VIF < 5)
• H6: CEBQ_ & DWOM_ show moderate correlation but remain distinct

Summary of Findings

1. DWOM_ has the strongest influence on BEPGZ_ (path coefficient = 4489).
2. R² for BEPGZ_ is high (0.7696), indicating strong predictive power.
3. HTMT values show good discriminant validity, though some constructs exhibit high correlation.
4. Effect size (f²) confirms DWOM_’s significant impact.
5. Residuals indicate overall strong model accuracy, except for some items needing minor adjustments.

This study provides empirical insights into the evolving role of Generation Z’s digital engagement behaviors in shaping brand equity in the digital era. Through PLS-SEM analysis in SmartPLS 4, the findings highlight the strong predictive capability of Consumer Engagement Behaviors (CEBQ_), Digital Brand Engagement (DBGZ_), and Word-of-Mouth Marketing (DWOM_) in influencing Brand Equity Perceived Growth (BEPGZ_).

The analysis reveals that DWOM_ (0.4489) has the strongest positive effect on BEPGZ_, emphasizing the power of peer-driven brand advocacy and digital word-of-mouth. CEBQ_ (0.3044) and DBGZ_ (0.2579) further contribute to brand equity growth, indicating the importance of consumer interaction and digital engagement. The model explains 76.96% of the variance in BEPGZ_, confirming its high predictive strength. Reliability and validity tests demonstrate strong internal consistency, while collinearity diagnostics and discriminant validity assessments affirm construct robustness.

The findings underscore the need for brands to actively engage with Gen Z through social media platforms, interactive digital experiences, and peer-driven strategies. Marketers should focus on strengthening consumer participation and leveraging digital word-of-mouth to enhance brand perception and loyalty.

Future research should explore moderation effects across different demographic groups, platform-specific engagement strategies, and longitudinal studies to assess brand equity evolution over time. Additionally, AI-powered sentiment analysis and machine learning-driven predictive models could enhance understanding of digital consumer engagement, optimizing brand strategies for long-term growth in an era dominated by Generation Z.

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