The Impact Of Generative Ai Advertising On Consumer Acceptance: The Mediating Roles Of Perceived Authenticity And Perceived Ethical Risk

Abstract

Objective: The integration of Generative Artificial Intelligence (GenAI) in digital advertising triggers a paradigm shift toward hyper-personalization, introducing a theoretical tension between communication effectiveness and consumer ethical integrity. This study aims to comprehensively investigate the impact of Generative AI Advertising on consumer acceptance by extracting the dual mediating roles of perceived authenticity and perceived ethical risk. Method: Utilizing an explanatory quantitative research design, primary data were collected through a survey of 250 active marketplace users in Indonesia, specifically targeting the high-involvement electronic product segment. The data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results: Empirical findings confirm that GenAI Advertising exerts a positive and significant effect on perceived authenticity while simultaneously elevating perceived ethical risk. Perceived authenticity acts as a crucial antecedent that positively enhances consumer acceptance. Interestingly, contradicting traditional risk-avoidance assumptions, perceived ethical risk also exerts a significant positive effect on acceptance levels. This confirms the existence of a privacy paradox, wherein consumers willingly compromise ethical vulnerabilities in exchange for hyper-personalized utility. Both variables act as complementary partial mediators; nevertheless, GenAI-based advertising retains a direct, positive, and significant impact on consumer acceptance. The research model demonstrates substantial predictive power, explaining 66.4% of the variance. Conclusions: This study extends the Technology Acceptance Model (TAM) and Authenticity Theory by justifying that algorithmic advertising effectiveness relies on the firm's strategic orchestration in managing the ethics-authenticity paradox. Managerially, proactive algorithmic transparency is crucial as a credibility signal to navigate this privacy calculus in the era of digital hyper-reality