Does metaverse improve recommendations quality and customer trust? : a user-centric evaluation framework based on the cognitive-affective-behavioural theory

Recommendation agents (RAs) have proven to be effective decision-making tools for customers, as they can boost trust and loyalty when customers shop online. They can analyse large amounts of data using machine learning algorithms and predictive analytics capabilities to provide highly relevant recommendations to users. In previous studies, several approaches have been implemented to refine and assess the effectiveness of these agents. As a new form of virtual reality universe, metaverses can be seen as a new venue for improvements in the performance of online RAs. By exploiting the capabilities of the metaverse and incorporating data about the user's behaviour and preferences, the performance of these systems can be enhanced in terms of the accuracy, diversity, and novelty of the generated recommendations. The metaverse can provide visually appealing and interactive recommendations, and there are several potential factors that can affect the customer's experience. The cognitive-affective-behavioural theory is used to develop the proposed research model. This study investigates the impact of the capabilities of the metaverse on three quality factors of RAs: diversity, accuracy, and novelty. The influence of the quality of the recommendations on affective trust and the influence of affective trust on customer loyalty are also examined. In addition, as this is an emerging technology, perceived privacy plays a crucial role in maintaining users' trust and confidence. Hence, the moderating influence of perceived privacy on the relationship between the quality and affective trust of RAs is examined. The moderating impact of product knowledge on the relationship between the individual perception of trust and loyalty is investigated. Data were acquired from 288 Malaysian respondents and analysed using the PLS-SEM method. The findings of this study show that the capabilities of the metaverse have favorable impacts on several quality factors of the recommender system, including accuracy, diversity, and novelty. Furthermore, these quality factors impact the perceived quality of RAs, which in turn impacts customer trust and loyalty. Perceived privacy acts as a moderator on the relationship between the quality of recommendations and the individual's perception of trust.