Making Recommendations More Effective through Social Features: The Impact on Social E-commerce Users' Purchasing Intentions

Xu Li, Kanliang Wang, Qiqi Jiang

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


The interplay between social media and e-commerce is an emerging business model. Social recommendation, as an important part of social e-commerce, plays a key role in affecting consumer judgement and decision process. Despite increasing number of technical researches, the research in the role of design affordance of social display features in the context of social media driven e-commerce is still missing. Anchoring on signaling theory, we develop and validate our hypotheses using a series of online experiments. Our findings indicate the presentation of friends-based feedback can induce higher purchase intentions than presenting the crowd-based feedback. We further propose that this relationship is moderated by the temporal distance. Specifically, when the temporal distance is short, recommendations from friends are more persuasive than those from the crowd, whereas the opposite occurs for users at the longer temporal distance. Our findings have important implications for research and practice in designing effective recommendation systems.
Original languageEnglish
Title of host publicationPACIS 2021 Proceedings
EditorsDoug Vogel, Kathy Ning Shen, Pan Shan Ling, M.N. Ravishankar, Xi (Jacky) Zhang
Number of pages14
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
Publication date2021
Article number346
Publication statusPublished - 2021
EventThe 25th Pacific Asia Conference on Information Systems. PACIS 2021 - Virtual
Duration: 12 Jul 202114 Jul 2021
Conference number: 25


ConferenceThe 25th Pacific Asia Conference on Information Systems. PACIS 2021
Internet address


  • Social recommendation
  • Social features
  • Signaling theory
  • Psychological distance
  • Social E-Commerce

Cite this