Factors influencing trust in sellers, trust in community and fashion purchase intention in social commerce in Viet Nam

2026-02-24T09:40:44+07:00

In Vietnam, social media has become deeply integrated into commercial activities, giving rise to social commerce as a community-driven transaction model in which trust is a key factor influencing consumers’ purchasing behavior, particularly in the fashion industry. Grounded in the Stimulus–Organism–Response (S–O–R) framework and trust transfer theory, this study examines how social commerce characteristics (information quality, reputation, and interaction with sellers) and social commerce constructs (ratings and reviews, community, recommendations, and referrals) influence trust in sellers and trust in community and how these two trust objects subsequently affect fashion purchase intention. Data were collected from 568 valid survey responses and analyzed using the partial least squares structural equation modeling (PLS-SEM) approach. The results indicate that all proposed antecedents have positive and statistically significant effects on their corresponding trust constructs. A notable finding is that trust in community positively influences trust in sellers, confirming the trust transfer mechanism whereby community-level trust serves as a foundation for seller-level trust. Furthermore, both trust in sellers and trust in community are found to have positive impacts on fashion purchase intention. This study provides empirical evidence within the Vietnamese context and offers managerial implications for fashion businesses operating on social commerce platforms.

Exploring the relationship between AI service quality dimensions and consumers’ perceived competence: a study in the online retail industry

2026-04-02T07:28:50+07:00

This study examines how artificial intelligence (AI) service quality dimensions influence consumers’ perceived competence in the online retail sector. Based on an online survey of 411 consumers in Hue City, and using multivariate regression analysis, the study found that all four dimensions of AI service quality, including technical security, technical reliability, perceived personalization, and hyperconnectivity, significantly and positively affect perceived competence. Perceived personalization emerged as the strongest predictor (beta = 0.357), indicating that highly tailored experiences primarily drive consumers’ evaluations of AI intelligence. Technical reliability (beta = 0.299), hyperconnectivity service capability (beta = 0.278), and technical security (beta = 0.257) also demonstrated substantial positive impacts. These findings highlight the crucial role of AI’s functional and personalized attributes in shaping consumer perceptions of its capability. The study offers theoretical insights by exploring how AI service quality affects perceived competence in an emerging market and provides practical implications for retailers aiming to enhance AI adoption and consumer engagement in the online retail industry.

Hue University Journal of Science: Economics and Development

Factors influencing trust in sellers, trust in community and fashion purchase intention in social commerce in Viet Nam

Exploring the relationship between AI service quality dimensions and consumers’ perceived competence: a study in the online retail industry