Dampak Pemasaran Influencer terhadap Keputusan Pembelian Impulsif Generasi Z yang Dimoderasi Tingkat Kepercayaan Konsumen - Wa Ode Nursaadha Rajuddin; Ifin Naim; Herman Titop (FKDP (Forum Komunikasi Dosen Peneliti), 2026)
Spontaneous buying behavior in the digital era is currently dominated by content creators; however, the effectiveness of consumer cognitive filters in mitigating this urge remains highly debated. This study aims to analyze the effect of influencer marketing on impulsive buying behavior among Generation Z in Kendari, incorporating consumer trust as a moderating variable. Employing a quantitative approach, primary data were gathered from 190 respondents selected through a purposive sampling technique. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) via the SmartPLS software. Measurement model evaluation indicated that the research instrument fulfilled the criteria for convergent validity, discriminant validity (HTMT < 0.85), and was free from multicollinearity issues (VIF < 5.00). The structural model assessment (bootstrapping) revealed that influencer marketing exerts a highly dominant and statistically significant positive effect on impulsive buying behavior (ß = 0.828; t = 27.649; p = 0.000). Conversely, consumer trust partially has no significant effect on impulsive buying (ß = 0.004; p = 0.928). Furthermore, interaction effect testing demonstrated that consumer trust fails to moderate the relationship between influencer marketing and impulsive buying behavior (ß = -0.099; t = 1.482; p = 0.139), thus rejecting the moderation hypothesis. Nonetheless, the framework exhibits a robust predictive capacity, yields an Adjusted R-Square of 66.8%, and establishes a large predictive relevance (Q2 = 0.389). These findings extend the Stimulus-Organism-Response (S-O-R) theory by revealing that in a social commerce ecosystem, aggressive external stimuli from influencers, coupled with the inherent FOMO syndrome of Generation Z, effectively override internal cognitive filters.