Comparative Analysis of Influencer Marketing Effectiveness Across Generations 18-65

Thesis Abstract

This study investigates how influencer marketing effectiveness varies across age cohorts spanning 18 to 65 years, addressing the gap in cross-generational insights for brands leveraging social media creators. The problem centers on the assumption of uniform influencer impact across generations, which may overlook distinct motivational drivers, trust pathways, and content preferences that shape engagement, perceived authenticity, and purchase intent. The aim is to compare influencer effectiveness across Generations Y (18–34), Gen X (35–50), and the Silent/Early Gen Z boundary (51–65) to identify differential effects on brand attitude, perceived credibility, engagement, and conversion. Specific objectives are (1) to quantify the relative impact of macro-influencer versus micro-influencer campaigns on brand attitude across generations; (2) to examine the mediating roles of perceived credibility, parasocial interaction, and message relevance in driving engagement and purchase intention; (3) to assess the moderating effects of platform (Instagram, TikTok, YouTube) and content format (video, image, story) on cross-generational effectiveness; (4) to develop a parsimonious cross-generational model predicting purchase intention from influencer metrics (reach, engagement rate, authenticity cues) and individual differences (digital nativity, privacy concerns). The study adopts a cross-sectional, mixed-methods design grounded in the Uses and Gratifications Theory and the Source Credibility Theory to explain why audiences differentially process influencer content. A multinomial sampling frame will target 1,200 respondents across three age strata 18–34 (n=400), 35–50 (n=400), and 51–65 (n=400), drawn from social media users in urban and semi-urban areas of a developed market. Data collection comprises two instruments (a) a structured online survey measuring variables including brand attitude, purchase intention, perceived credibility, parasocial interaction, message relevance, and influencer characteristics; (b) semi-structured interviews with 24 participants (8 per age group) to elicit qualitative insights on credibility cues, content interpretation, and purchase decision processes. The survey will include validated scales such as the Credibility Scale (McCroskey and Teven), the Parasocial Interaction Scale, and a modified Brand Attitude Inventory, with reliability targeted at Cronbach’s alpha ? 0.80. Data collection will occur over a 10-week window, ensuring demographic quotas are met. Quantitative analysis will employ structural equation modeling (SEM) to test the full hypothesized model, including mediation paths for perceived credibility and parasocial interaction, and moderation effects for platform and content format. Multi-group SEM will compare path coefficients across the three generational cohorts to identify statistically significant differences. Complementary analyses will include hierarchical regression to assess incremental variance explained by influencer characteristics beyond traditional advertising metrics, and ANOVA to detect mean differences in key outcomes by generation and platform. Qualitative data from interviews will be analyzed thematically using a coding framework derived from Uses and Gratifications Theory and Source Credibility Theory to triangulate and enrich interpretation of quantitative results. Key expected findings include (a) stronger effects of micro-influencers on engagement and purchase intention among younger cohorts, while mid-to-older cohorts show heightened sensitivity to credibility cues and authenticity signals; (b) platform and format interactions where short-form video on TikTok and Instagram Reels yields higher engagement for younger generations, whereas longer-form YouTube content aligns with credibility perceptions in older cohorts; (c) amplification of effect via parasocial relationship strength, particularly among younger respondents, and higher perceived message relevance boosting purchase intent across all generations but with varying magnitudes. The study contributes to knowledge by offering a validated cross-generational model for influencer effectiveness, integrating credibility and parasocial interaction as central mechanisms, and providing actionable guidance for marketers to tailor influencer strategies by generation, platform, and content format. The main conclusion posits that influencer marketing is not generationally uniform; effectiveness is contingent upon the alignment of influencer type, platform, and content with generational values and media consumption habits. Recommendations include developing generation-specific influencer selection criteria, prioritizing authenticity signals over reach for younger audiences, and leveraging platform-native formats that maximize perceived credibility for older consumers. Implications extend to measurement frameworks for influencer campaigns, suggesting the inclusion of cross-generational segmentation in impact assessments and budgeting decisions.