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.

中文翻译：

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metaverse 是否提高了推荐质量和客户信任度？基于认知-情感-行为理论的以用户为中心的评价框架


事实证明，推荐代理 （RA） 是客户的有效决策工具，因为它们可以提高客户在线购物时的信任和忠诚度。他们可以使用机器学习算法和预测分析功能分析大量数据，为用户提供高度相关的推荐。在以前的研究中，已经实施了几种方法来改进和评估这些药物的有效性。作为一种新形式的虚拟现实宇宙，元宇宙可以被视为改进在线 RA 性能的新场所。通过利用元宇宙的功能并整合有关用户行为和偏好的数据，这些系统的性能可以在生成推荐的准确性、多样性和新颖性方面得到增强。元宇宙可以提供视觉上吸引人的交互式推荐，并且有几个潜在因素会影响客户的体验。认知-情感-行为理论用于开发拟议的研究模型。本研究调查了元宇宙的能力对 RA 的三个质量因素的影响：多样性、准确性和新颖性。还研究了推荐质量对情感信任的影响以及情感信任对客户忠诚度的影响。此外，由于这是一项新兴技术，感知隐私在维护用户的信任和信心方面起着至关重要的作用。因此，研究了感知隐私对 RA 质量和情感信任之间关系的调节影响。研究了产品知识对个人信任感知和忠诚度之间关系的调节影响。 数据是从 288 名马来西亚受访者那里获得的，并使用 PLS-SEM 方法进行分析。这项研究的结果表明，元宇宙的能力对推荐系统的几个质量因素有有利影响，包括准确性、多样性和新颖性。此外，这些质量因素会影响 RA 的感知质量，进而影响客户的信任和忠诚度。感知隐私在推荐质量和个人对信任的看法之间的关系中起着调节作用。