Journal Article10.2307/25148784
E-commerce product recommendation agents: use, characteristics, and impact
Bo Sophia Xiao,Izak Benbasat +1 more
1.1K
TL;DR: A conceptual model with 28 propositions derived from five theoretical perspectives is developed that identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RA.
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Abstract: Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online They can reduce the information overload facing consumers, as well as the complexity of online searches Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications
Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars It also provides advice to information systems practitioners concerning the effective design and development of RAs
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Citations
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The Impact of Trust and Recommendation Quality on Adopting Interactive and Non-Interactive Recommendation Agents: A Meta-Analysis
TL;DR: In this article , the differences between interactive and non-interactive RAs were examined in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users' cognitive and affective attitudes and behavioral intention.
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Behavioral Effects in Consumer Evaluations of Recommendation Systems
Agapi Fytraki
- 01 Jan 2010
TL;DR: Focusing on technology itself, RA use and RA output, this dissertation confirms that the presentation of alternatives at the output stage, as well as the decision strategy implemented by the recommendation system itself, can enhance one’s evaluation of an RA.
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