Integrating tradeoff support in product search tools for e-commerce sites
Proceedings of the 6th ACM conference on Electronic commerce
How Positive Informational Social Influence Affects Consumers' Decision of Internet Shopping?
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Adaptive decision support system (ADSS) for B2C e-commerce
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Impact of social influence in e-commerce decision making
Proceedings of the ninth international conference on Electronic commerce
Case amazon: ratings and reviews as part of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
Feed me: motivating newcomer contribution in social network sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social recommender systems for web 2.0 folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Evaluating critiquing-based recommender agents
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Proceedings of the third ACM conference on Recommender systems
| Hi-index | 0.00 |
This paper aims at studying the roles of social information (as obtained from social networking sources) in buyers' decision process when they are searching for products to buy. Through both close observation of users' objective behavior and qualitative interview of their reflective comments, we have discovered the importance of different types of social information in supporting users to achieve a confident purchase decision at the end. Improving suggestions are further derived on how to better present the social information and combine them with static product attributes to enhance current e-commerce decision supports so as to optimally adapt to online buyers' actual information needs.