Mitigating seller uncertainty in social commerce platforms by exploring pre-purchase customer-seller signals

Abstract

In the growing social commerce landscape, addressing seller uncertainty has become critical for fostering consumer trust and enhancing purchase decisions. Seller uncertainty, often driven by information asymmetry and a lack of trust signals, can significantly hinder transactions on these platforms. This study investigates the mitigation of seller uncertainty in social commerce platforms by exploring pre-purchase customer-seller signals. Utilizing signaling theory, the research examines the role of information quality, positive reviews, seller-reputation, seller-popularity, and return policy leniency in reducing uncertainty. A hybrid SEM-ANN approach was employed to rigorously examine these relationships. Data was gathered from 368 social commerce users, providing robust insights into the factors that most significantly influence seller uncertainty. The findings reveal that all five signals play a significant role in reducing seller-uncertainty, with information quality emerging as the most critical factor. This emphasizes the need for sellers to provide clear, accurate, and comprehensive information to alleviate buyer concerns. The findings contribute theoretically and practically by deepening the understanding of uncertainty reduction mechanisms in social commerce and providing valuable insights for sellers and platform designers.