Evaluating the Effectiveness of Social Recommender Systems on Consumer Satisfaction and Purchase Intentions

Abstract

With the widespread adoption of social media platforms, consumers increasingly rely on recommendations from social networks. Simultaneously, mutual recommendations among family and friends also play a significant role in purchase decisions. Social recommendation systems analyze users' social networks, behavioral data, and interest preferences to recommend personalized products or services. This method enhances the accuracy and relevance of recommendations, increases users' trust and satisfaction, and boosts merchants' revenues. This study aims to investigate the impact of social recommendation systems (such as friend recommendations and social media recommendations) on consumer purchase decisions. The primary objectives of this research include: Firstly, examining the role of friend recommendations in the consumer decision-making process and exploring their impact on enhancing consumers' trust and purchase intentions. Secondly, studying the accuracy of recommendation algorithms to understand their effect on meeting consumer needs and influencing purchase intentions. Thirdly Based on the research findings, providing reference suggestions for optimizing recommendation systems to e-commerce platforms, brick-and-mortar retailers, and merchants, assisting them in formulating precise marketing strategies. Through this study, we hope to offer new insights to both academia and industry, promoting the further development and application of social recommendation systems. Additionally, we aim to provide valuable theoretical support and practical guidance for optimizing recommendation systems on e-commerce platforms, for brick-and-mortar retailers, and for merchants.