AI-PERSONALIZED SOCIAL COMMERCE AND GENERATION Z LOCAL BRAND LOYALTY: EXPERIENCE AND E-TRUST

Abstract

Purpose: This conceptual article explains how artificial-intelligence-based personalization within social commerce can build durable loyalty toward local brands among Generation Z. It addresses fragmented research that examines personalization, customer experience, electronic trust, and loyalty in separate causal models.

Research Methodology: This integrative conceptual review synthesized 57 peer-reviewed journal articles published from 2000 to July 2026, while retaining earlier seminal relationship-marketing sources. Reproducible search blocks, explicit inclusion and exclusion criteria, backward and forward citation tracing, DOI verification, and concept coding were used to compare definitions, analytical levels, mechanisms, and boundary conditions across six literature streams.

Results: The synthesis defines AI-personalized social commerce as an adaptive capability that configures recommendations, content, offers, conversational support, and social evidence within commerce-enabled social environments. Six propositions indicate that this capability improves digital customer experience and electronic trust; experience strengthens trust and local brand loyalty; trust supports loyalty; and experience followed by trust forms the principal serial pathway. Transparency, perceived data control, platform credibility, product involvement, and local-identity salience delimit these relationships.

Limitations: This theory-synthesis review is limited to a traceable English-language journal corpus and does not estimate effects or establish causal order. The proposed framework therefore requires scale validation and longitudinal, experimental, and field-based testing across social-commerce platforms and product categories.

Contribution: Recommendation accuracy alone cannot secure loyalty. Local brands must convert algorithmic relevance into coherent encounters and credible evidence of competence, integrity, and acceptable data conduct. Managers should combine explainable recommendations, editable preference controls, authentic social proof, and consistent fulfillment. The framework connects AI marketing and social commerce with customer-experience, signaling, and commitment-trust theory, while explaining local brand loyalty as a sequentially produced outcome rather than an immediate response to targeting.