Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
Ineractive 3D presentations and buyer behavior
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Preserving privacy when preference searching in e-commerce
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Designing personalized user experiences in eCommerce
A cognitive approach for agent-based personalized recommendation
Knowledge-Based Systems
Recommendation from robots in a real-world retail shop
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
Future research challenges and applications of ubiquitous robotics
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Ubiquitous robotics: Recent challenges and future trends
Robotics and Autonomous Systems
| Hi-index | 0.00 |
This paper investigates the potential of recommendation agents for electronic shopping to influence human decision making by shaping user preferences. Specifically, we examine how the type of information that is elicited by a shopping agent for use in its recommendation algorithm may affect consumers'preference for product features and ultimately their product choice in an electronic marketplace. A recommendation agent is defined as a software tool that (a) calibrates a model of a user's preference based on his/her input and (b) uses this model to make personalized product recommendations. We report the results of a controlled experiment that demonstrates that, everything else being equal, the inclusion of a product feature in a recommendation agent renders this feature more prominent in shoppers'purchase decisions. In addition, we find that this effect is moderated by an important property of the marketplace - the correlation structure among the features of available products. We conclude that electronic shopping agents, through the design of their recommendation algorithms, have the potential to influence user preferences in a systematic fashion.