Building Digital Bonds: The Impact of AI-Driven Personalization on Customer Experience and AI Relationship Quality
DOI:
https://doi.org/10.64534/z83n4978Keywords:
AI-driven personalization, AI-enabled customer experience, perceived AI relationship quality, trust in AI, perceived personal relevance, privacy concerns, technology readiness.Abstract
This study aims to examine the impact of AI-driven personalization on AI-enabled customer experience and perceived AI relationship quality. It further investigates the mediating roles of trust in AI and perceived personal relevance, as well as the moderating effects of privacy concern and technology readiness within an integrated conceptual framework. A quantitative, cross-sectional research design was employed. Data were collected from 330 users who had prior experience with at least one of three specified AI-enabled digital platform categories: e-commerce recommendation platforms (e.g., online retail sites), streaming services (e.g., video or music platforms), and fintech applications (e.g., digital banking or payment apps). Respondents were screened using platform-specific filter questions to reduce heterogeneity. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS. Reliability, validity, mediation, and moderation effects were evaluated using bootstrapping techniques. To address potential common method bias arising from single-source self-reported data, Harman's Single-Factor test was conducted. The test confirmed that no single factor accounted for the majority of variance, suggesting that CMB does not critically threaten the validity of the findings.
The main finding is that AI-driven personalization significantly enhances AI-enabled customer experience (β = 0.684, p < 0.001), with Trust in AI emerging as the primary psychological mediator of both experiential and relational outcomes. Perceived personal relevance provided a complementary mediation pathway. Privacy concern and technology readiness acted as modest boundary conditions on the personalization–mediator relationships. Additionally, privacy concern and technology readiness were found to act as boundary conditions that modestly moderated the personalization–mediator relationships, consistent with Privacy Calculus Theory and Technology Readiness Theory respectively. This study provides a comprehensive model integrating experiential, relational, and individual-difference perspectives in AI-enabled services. The findings offer theoretical advancement and practical insights for designing transparent, user-centric, and trust-enhancing AI personalization strategies.
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