Psychological Determinants of Consumer Acceptance of AI Marketing Technologies: A TRAM Analysis Using SmartPLS

Abstract

In this study, we use a hybrid model that combines the Parasuraman Technology Readiness (TR) index and the Davis Technology Acceptance Model (TAM) to examine the psychological factors that influence consumers' willingness to adopt AI marketing technologies. Chatbots driven by artificial intelligence, customized recommendation engines, predictive analytics ads, and AI-enabled voice assistants were the four settings used to recruit 487 customers for a cross-sectional quantitative survey. Parametric Least Squares Structural Equation Modeling (PLS-SEM) implemented in SmartPLS 4.0 was used for data analysis. Composite reliability (CR), heterotrait-monotrait ratios (HTMT), average variance extracted (AVE), and discriminant validity were all shown in the assessment of the measurement model. Perceived usefulness (b = 0.312, p <.01) and perceived ease of use (b = 0.287, p <.01) were both positively impacted by technological readiness, according to the structures model. Attitude toward AI was impacted by both perceived usefulness and perceived ease of use, which in turn were predicted by perceived usefulness (b = 0.401, p <.001) and perceived ease of use (PU: b = 0.356; PEOU: b = 0.298). Attitude was the primary factor in determining actual use behavior, and it was the strongest predictor of behavioral intention (b = 0.443, p <.001). A total indirect impact of 0.335 (95% CI [0.261, 0.413]) was shown by the mediation analysis, which demonstrated that TAM constructs entirely mediated the link between TR-behavioral intention. Generation Z had the greatest route coefficients throughout the model, indicating a substantial generational difference, according to multi-group analysis. In addition to providing useful implications for the strategy for adopting new technologies, these findings advance TRAM theory within the context of AI marketing.