Examining the Role of Trust and Privacy Effects Through Online Reviews in Social Commerce Using an Integrated Model and Hybrid Approach Analysis

Social commerce (s-commerce) is increasing in importance in electronic commerce (e-commerce), as new platforms emerge that combine social media features with the attributes of traditional commerce to optimize consumers' purchase experiences and social interaction. However, privacy and trust issues in the online environment are rising and have become increasingly important, due to the huge amount of user-generated content (UGC) and the lack of face-to-face interactions. Therefore, understanding how the security factors of online reviews could impact on s-commerce decision making is a critical need. Drawing upon the task–technology fit (TTF) model, institution-based trust, and the heuristic–systematic model (HSM), this research established a research framework to better understand online review factors, trust, and privacy concern and how they affect consumers' behavioral intention to use s-commerce sites. In total, 486 online survey responses were collected from s-commerce users. This research used structural equation modeling (SEM) and artificial neural network (ANN) analysis to analyze the data and to confirm the hypotheses proposed in the research model. The empirical findings revealed that source credibility, information quality, and influencer endorsement positively and significantly impacted on trust in s-commerce sites. However, the results showed that reviews did not significantly impact on trust in s-commerce sites. Privacy concern was also revealed to negatively affect both trust in s-commerce sites and behavioral intention. The results also showed that trust in s-commerce sites and task–technology fit (TTF) have a significant impact on behavioral intention. Finally, this research discussed its theoretical, practical, and social contributions, as well as its limitations, and suggested directions for future research.