Generative Artificial Intelligence in Marketing Practice: An Empirical TAM-UTAUT2 PLS-SEM Study of Consumer Acceptance and Trust

Abstract

Abstract

Background: Generative artificial intelligence (GenAI) has emerged as a transformative force in marketing, enabling content creation, personalization, and customer engagement at unprecedented scale. Yet the rapid commercial deployment of GenAI capabilities has outpaced empirically grounded understanding of the factors driving consumer acceptance, trust formation, and sustained behavioral engagement with AI-mediated marketing interactions.

Objective: This study develops and empirically tests an integrated theoretical model — combining the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Trust-Based Relationship Marketing Theory — to explain consumers' behavioral intentions toward GenAI-mediated marketing interactions.

Method: A cross-sectional online survey was administered to N = 387 active e-commerce consumers (Mage = 31.2, SD = 8.4; 52.7% female) with prior exposure to AI-generated marketing content, recruited through stratified quota sampling in three countries (México, Colombia, España). PLS-SEM was employed using SmartPLS 4.0 with 5,000 bootstrap resamples. A Common Method Bias assessment using the marker variable technique was conducted.

Results: The measurement model demonstrated strong psychometric properties (all AVE ≥ .51; ρc ≥ .87; HTMT < .85). The structural model explained 58.4% of the variance in Behavioral Intention to Engage with GenAI Marketing (BI; R² = .584) and 47.1% in AI Brand Trust (ABT; R² = .471). Perceived Usefulness (β = .412, p < .001) was the strongest predictor of BI, followed by AI Brand Trust (β = .334, p < .001) and Hedonic Motivation (β = .218, p = .003). Privacy Concern significantly attenuated BI (β = −.261, p < .001). Content Authenticity Perception partially mediated the Perceived Usefulness → BI path (indirect β = .143, 95% CI [.098, .193]). Significant generational differences were found via multigroup analysis (Gen Z vs. Millennials vs. Gen X).

Conclusions: Consumer acceptance of GenAI in marketing is driven by a complex interplay of utilitarian, hedonic, and trust-based mechanisms, substantially moderated by privacy concerns and content authenticity perceptions. The findings have direct implications for GenAI deployment strategy, ethical design, and consumer protection regulation.

Resumen

Antecedentes: La inteligencia artificial generativa (IAG) ha emergido como fuerza transformadora en el marketing. No obstante, el acelerado despliegue comercial de las capacidades de IAG ha superado la comprensión empírica de los factores que impulsan la aceptación del consumidor, la formación de confianza y el compromiso conductual sostenido. Objetivo: Este estudio desarrolla y contrasta empíricamente un modelo teórico integrado — que combina el TAM, el UTAUT2 y la Teoría de Marketing Relacional Basada en Confianza — para explicar las intenciones conductuales de los consumidores hacia las interacciones de marketing mediadas por IAG. Método: Se administró una encuesta transversal en línea a N = 387 consumidores activos de e-commerce con exposición previa a contenidos de marketing generados por IA (Medad = 31.2, DT = 8.4; 52.7% mujeres) en tres países. Resultados: El modelo estructural explicó el 58.4% de la varianza en la Intención Conductual (R² = .584) y el 47.1% en la Confianza en Marca IA (R² = .471). La Utilidad Percibida fue el predictor más fuerte (β = .412, p < .001). La Preocupación por Privacidad atenuó significativamente la intención (β = −.261, p < .001).

Keywords: generative AI; TAM; UTAUT2; consumer acceptance; AI brand trust; privacy concern; content authenticity; PLS-SEM; marketing ethics; behavioral intention

Palabras clave: IA generativa; TAM; UTAUT2; aceptación del consumidor; confianza en marca IA; preocupación por privacidad; autenticidad del contenido; PLS-SEM; ética del marketing; intención conductual

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