The impact of AI-driven social media marketing activities on gen z online purchase intention: The mediating role of perceived personalization and the moderating roles of privacy concern and AI trust

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Authors: Mohammad Abuhashesh, Shafig Al-haddad, Amer Badran, Abdel‐Aziz Ahmad Sharabati
Cited by: 1
References: 1

Abstract

This research paper explores the effect of AI-enabled social media marketing practices on the online buying intentions of Generation Z. The authors gathered 314 answers, with only 299 responses being usable for analysis. The research population includes Gen Z individuals who utilize social media. The research paper uses a quantitative, descriptive method and convenience sampling. Structural Equation Modelling (SEM) through SPSS software was used to test hypotheses. The study findings demonstrate that using AI-based social media applications in marketing positively influences intention to purchase online and perceived personalization. However, perceived personalization negatively affects the intention to make an unexpected purchase. This suggests that AI-generated content focused on personalization may be perceived as highly targeted and can raise concerns about privacy and manipulation, ultimately reducing the likelihood of purchase. Concerning the dimensions, the purchase intention is significantly influenced by the interaction, ad. Personalization and AI-driven e-WOM. At the same time, for entertainment and trendiness, the effects are null and negative, respectively. Privacy concerns and AI trust are of particular significance in enhancing the model's explanatory power, with AI trust being a powerful predictor of effective personalization. AI-driven social marketing remains an effective marketing tool, particularly for Gen Z, when it is interactive, socially innovative, and perceived as trustworthy and protective of privacy.

Fields of Study & Subjects

AI in Service Interactions Digital Marketing and Social Media Technology Adoption and User Behaviour

Publication details

Journal / source
International Journal of Data and Network Science
Publisher
Growing Science
Volume
10
Issue
4
Pages
2095–2108
Publication date
1 January 2026
Language
EN