THE IMPACT OF INFLUENCER MARKETING ON GEN Z CONSUMER BEHAVIOUR

International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 151 ISSN 2394 - 7780 THE IMPACT OF INFLUENCER MARKETING ON GEN Z CONSUMER BEHAVIOUR Dr. G Venkat Kista Reddy1, Basani Asher Reddy2, Raj Gattamaneni3, G. Ashish Varma4 and Tanniru Adarsh5 1Associate Professor, Koneru Lakshmaiah Educational Foundation 2,3,4,5BBA 3rd year, Koneru Lakshmaiah Educational Foundation ABSTRACT This research investigates the impact of influencer marketing on the purchasing behaviour of Generation Z consumers. The study draws on the Source Credibility Model, Parasocial Interaction Theory, and Social Influence Theory to evaluate how perceived credibility, authenticity, and parasocial relationships shape purchase intentions. Using a structured online questionnaire (Google Forms) distributed among Indian and international Gen Z respondents (N = 40 attention-check passers), quantitative analysis was conducted through Python and Excel. Results show that influencer credibility and parasocial connection significantly and positively predict purchase intention, while authenticity demonstrates a negative coefficient when other factors are controlled, suggesting suppression effects. The findings contribute to understanding how trust and relational attachment drive consumer behaviour in a digital economy and highlight practical implications for brands leveraging influencer collaborations. Keywords: Influencer Marketing, Gen Z, Social Media, Consumer Behaviour, Credibility, Parasocial Interaction, Source Credibility INTRODUCTION Over the past decade, influencer marketing has transformed how brands communicate with consumers. Social media influencers have become a vital link between companies and digital audiences, especially Generation Z— individuals born between 1997 and 2012—who exhibit distinctive online consumption patterns. Traditional advertising increasingly fails to resonate with this demographic; Gen Z prefers authentic voices, peer recommendations, and relatable digital personalities. India, with one of the world’s largest Gen Z populations and a thriving creator economy, provides fertile ground for studying this phenomenon. Platforms such as Instagram, YouTube, and TikTok (globally) have redefined opinion leadership. Despite global research on influencer effects, there remains a need to empirically quantify how credibility, authenticity, and parasocial interaction affect actual purchasing behaviour among Indian and global Gen Z audiences. This study therefore addresses the research question: How do influencer credibility, authenticity, and parasocial interaction influence Gen Z consumers’ purchase intentions? LITERATURE REVIEW 1. Source Credibility Model According to Hovland and Weiss (1951), persuasive communication effectiveness depends on the perceived credibility of the source. Ohanian (1990) later defined credibility through three dimensions—expertise, trustworthiness, and attractiveness. In influencer marketing, credibility stems from an influencer’s domain knowledge and transparency, leading followers to accept recommendations as reliable. 2. Parasocial Interaction (PSI) Theory Developed by Horton and Wohl (1956), PSI describes the one-sided emotional relationships audiences form with media figures. In digital platforms, followers often feel personally connected to influencers, perceiving friendship or intimacy that enhances persuasion and purchase intent (Labrecque 2014). 3. Social Influence Theory Kelman (1958) proposed three mechanisms—compliance, identification, and internalization—through which individuals conform to influencers’ expectations. Gen Z consumers often identify with influencer lifestyles, integrating recommendations into their self-concept. 4. Consumer Engagement Model Brodie et al. (2013) emphasized emotional, cognitive, and behavioral engagement. Influencers engage audiences through storytelling and interactive features, creating brand communities that boost loyalty. International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 152 ISSN 2394 - 7780 5. Empirical Findings Global studies (Casaló et al., 2018; Lou & Yuan, 2019) show credibility as a consistent predictor of purchase intention. Indian research (Chopra & Agarwal, 2021) echoes this, but notes authenticity fatigue among Gen Z. Gaps remain in quantifying these constructs jointly for Indian and global contexts—addressed by this paper. METHODOLOGY Research Design A quantitative, cross-sectional design was adopted. The questionnaire measured six latent constructs: Credibility, Authenticity, Parasocial Interaction (PSI), Message Value, Skepticism, and Purchase Intention. Sampling and Data Collection Responses were collected via Google Forms using convenience sampling targeting Gen Z participants in India and abroad. Of 55 total responses, 40 valid attention-passer cases (L53 = 4) were retained. Instrument All items were rated on a five-point Likert scale (1 = Strongly Disagree → 5 = Strongly Agree). The survey drew on validated scales from prior literature (Ohanian, 1990; Kim & Kim, 2020). Demographics Most respondents were students aged 18–25; ~58 % female, 42 % male; major platforms: Instagram (72 %), YouTube (20 %), others (8 %). Data Analysis Python’s Statsmodels package and Excel were used for reliability, descriptive, correlation, and regression analyses. RESULTS Reliability Analysis All scales exceeded Cronbach’s α ≥ 0.7: Credibility 0.885, Message Value 0.884, Purchase 0.796, Skepticism 0.788, PSI 0.763, Authenticity 0.720—demonstrating strong internal consistency. Descriptive Statistics Average scores: Credibility 2.84, Authenticity 3.42, PSI 3.52, Message Value 3.98, Skepticism 3.27, Purchase 3.09—indicating moderate-high perceptions of influencers and moderate buying intent. Correlations Credibility (r = 0.48, p < .01) and PSI (r = 0.43, p < .01) showed the strongest positive associations with Purchase Intention. Authenticity’s r = 0.15 was smaller but positive. Correlation Heatmap of Constructs Figure 1: Correlation heatmap showing relationships among Credibility, Authenticity, PSI, Message Value, Skepticism, and Purchase Intention (N = 40). International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 153 ISSN 2394 - 7780 Regression Analysis (Attention-Passers N = 40) Predictor B SE t p 95 % CI Constant 2.37 0.56 4.25 < 0.001 [1.24, 3.51] Credibility 0.62 0.18 3.43 0.0015 [0.25, 0.99] Authenticity −0.91 0.24 −3.78 0.0006 [−1.40, −0.42] PSI 0.43 0.18 2.40 0.0218 [0.07, 0.79] Credibility and PSI positively influenced Purchase Intention, while Authenticity was negatively signed under control, suggesting a suppression effect. Scatter Plot of Purchase vs Credibility (Attention-Passers) Figure 2: Scatter plot depicting the positive relationship between influencer credibility and purchase intention among Gen Z respondents (N = 40). Credibility Score Distribution Figure 3: Distribution of Credibility scores representing perceived expertise and trustworthiness of influencers. International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 154 ISSN 2394 - 7780 Authenticity Score Distribution Figure 4: Distribution of Authenticity scores indicating followers’ perceptions of influencer genuineness. PSI Score Distribution Figure 5: Distribution of Parasocial Interaction (PSI) scores showing relational bonds with influencers. Message Value Distribution Figure 6: Distribution of Message Value scores representing informative and entertaining content quality. International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 155 ISSN 2394 - 7780 Skepticism Score Distribution Figure 7: Distribution of Skepticism scores reflecting consumer awareness and critical evaluation of influencer content. Purchase Intention Distribution Figure 8: Distribution of Purchase Intention scores summarizing the likelihood of buying based on influencer recommendations. DISCUSSION Academic Implications Findings reinforce the Source Credibility Model and PSI Theory within a Gen Z social-media context. Credibility’s strong coefficient highlights expertise and trustworthiness as decisive cues in digital persuasion. PSI’s significance validates emotional attachment as a driver of behavioural intention. The negative coefficient for authenticity may reflect multicollinearity; once credibility and PSI are controlled, “authenticity” might capture over-curation or perceived inauthentic self-presentation—an emerging paradox in influencer research. This suggests future models should separate perceived genuineness from performative transparency. Managerial Implications (India + Global) For Indian brands, selecting influencers with genuine expertise—especially micro-creators in fashion, tech, and lifestyle—can yield stronger engagement than celebrity endorsements. Internationally, campaigns emphasizing relational storytelling (e.g., community challenges, co-creation content) outperform product-centric posts. Brands Should: ● Vet influencers on consistency and ethical transparency. ● Encourage moderate self-disclosure and domain-based expertise. ● Balance promotional and educational content to maintain credibility. International Journal of Advance and Innovative Research Volume 12, Issue 4 October - December 2025 156 ISSN 2394 - 7780 For influencers, cultivating sustained parasocial bonds through audience interaction, Q&A sessions, and authentic brand alignment will deepen trust. CONCLUSION This study demonstrates that influencer credibility and parasocial interaction significantly shape Gen Z purchase intentions, whereas authenticity’s impact is complex. The results suggest that expertise and perceived relational closeness drive trust and conversion, but over-engineered authenticity may reduce persuasiveness. Limitations: small sample (N = 40), self-reported responses, and cross-sectional design. Future research: test mediation models (Credibility → PSI → Purchase), expand sample across platforms and regions, and employ longitudinal designs to observe changing Gen Z attitudes. Overall, the study contributes to digital marketing scholarship and provides actionable insights for influencer strategy formulation. REFERENCES (APA 7ᵗʰ EDITION) • Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2013). Customer engagement conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271. • Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2018). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510–519. • Chopra, S., & Agarwal, S. (2021). Impact of influencer marketing on consumer buying behaviour in India. 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