Trust Through Recommendation in E-commerce

Maria Saxborn, Yuechen Pan, Alan Said. 2024, "Trust Through Recommendation in E-commerce". To Appear in Proceedings of the 2024 Conference on Human Information Interaction and Retrieval.


We explore the influence of recommender systems on trust among consumers in the fashion e-commerce domain. Anchoring on the Trust Building Model (TBM) , we investigate its adaptability and applicability in the context of interactive communication in recommender systems. Primarily leaning on qualitative data collection methods, namely semi-structured interviews, our work evaluates the classic TBM components – structure assurance, perceived reputation, perceived site quality, perceived web risk, trusting belief, and behavioral intention – affirming their relevance to recommender systems. Furthermore, new components, i.e., perceived service and recommendation quality, previous experience, perceived enjoyment, perceived recommendation authenticity, and intention to share interaction data, were examined in the context of recommender systems. Significantly, our study unveils that trusting beliefs can notably influence TBM’s preliminary behavioral intentions, with the competence belief having the most substantial impact, challenging the conventional TBM findings. The outcomes highlight that consumers place heightened value on the tangible provisions from the company over ethics-based factors like integrity. The proposed refined TBM offers potential in enhancing recommender systems in fashion e-commerce, facilitating a better understanding of consumer behavior and trust dynamics.

To Appear in Proceedings of the 2024 Conference on Human Information Interaction and Retrieval