Influencer Marketing and Electronic Word of Mouth on Skincare Purchase Decisions Among Generation Z Students in Indonesia
Abstract
Indonesia’s skincare market has grown at a pace that few industries match, and Generation Z is largely driving it. These young consumers do not browse catalogues or watch television commercials; they scroll through TikTok tutorials, weigh influencer endorsements, and read peer reviews before committing to a purchase. Yet surprisingly few empirical studies have examined influencer marketing and Electronic Word of Mouth (E-WoM) simultaneously within the Indonesian university context. This study does exactly that. Using a quantitative cross-sectional design, data were collected from 400 Generation Z university students recruited through TikTok and Instagram, with the minimum sample size established via the Cochran formula. All constructs were measured with a five-point Likert instrument adapted from validated scales, and hypotheses were tested through Structural Equation Modeling Partial Least Squares (SEM-PLS) in SmartPLS 4.0. The outer model confirmed full convergent and discriminant validity (outer loadings > 0.708; AVE > 0.50), and reliability was strong across all constructs (Cronbach’s Alpha and Composite Reliability both > 0.70). Collinearity between predictors was negligible (VIF = 1.010). Both influencer marketing (β = 0.173, p = 0.001) and E-WoM (β = 0.150, p = 0.037) exerted independent, statistically significant effects on purchase decisions, jointly accounting for 5.8% of explained variance (R² = 0.058). Crucially, PLS predictive analysis confirmed high out-of-sample predictive power: PLS-SEM RMSE was lower than the naïve linear model benchmark for every one of the five Purchase Decision indicators, a result that validates the model’s practical utility beyond its statistical significance. For Indonesian skincare brands, the takeaway is clear: influencer credibility and authentic consumer reviews are not interchangeable tools but distinct, complementary pathways to purchase that work best when deployed together.
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