How personalized Augmented Reality experiences influence consumer trust and repurchase intention: Evidence from Tabuk, Saudi Arabia

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Type of the article: Research Article

Abstract
Augmented Reality has emerged as a powerful marketing tool that is transforming online shopping by making products more tangible and engaging; however, there remains limited empirical evidence of understanding its personalized effects on consumer trust and repurchase intention. This study investigates how personalized augmented reality experiences influence consumer trust and repurchase intention among consumers in Tabuk, Saudi Arabia, proposing consumer trust as a mediating variable. Data were collected through an online survey conducted between September and October 2025, using random sampling techniques to select 152 consumers in Tabuk who are familiar with mobile shopping and augmented reality applications through diverse platforms such as Instagram, Snapchat, TikTok, WhatsApp, and shopping centers, to ensure heterogeneity and consistency. Analytical methods included path analysis, correlation analysis, and bootstrapping techniques. The findings revealed that personalized augmented reality experiences significantly enhance consumer trust (R² = 0.573) and positively influence repurchase intention (β = 0.331, p < .001). Furthermore, consumer trust proved a strong predictor of repurchase intention (R² = 0.691), indicating that consumer trust is a powerful driver of repurchase intention. Finally, the mediation model confirmed that consumer trust partially mediates the relationship between personalized augmented reality and repurchase intention, with the overall model explaining 74.9% of the variance. Theoretically, this study extends Stimulus-Organism-Response theory by integrating personalization, consumer trust, and repurchase intention into one holistic model. Practically, it offers actionable insights to retailers, marketers, and augmented reality developers in Saudi Arabia by identifying how to balance customization to foster trust and loyalty.

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    • Figure 1. Conceptual framework
    • Figure 2. Structural model with path coefficients
    • Figure 3. Mediation model (direct and indirect effects)
    • Table 1. Measurement items and constructs source
    • Table 2. Demographic and behavioral characteristics of respondents
    • Table 3. Descriptive statistics of scale items
    • Table 4. Descriptive statistics and scale reliability
    • Table 5. Inter-construct correlations and discriminant validity
    • Table 6. Results of hypothesis testing (path analysis)
    • Table A1. Section one: general information about the individual
    • Table A2. Section two: study variables
    • Conceptualization
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    • Data curation
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    • Formal Analysis
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    • Investigation
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    • Methodology
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