MISQ Archivist
E-Commerce Product Recommendation
Agents: Use, Characteristics, and Impact
Bo Xiao and Izak Benbasat
Abstract
Recommendation
agents (RAs) are software that elicits 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 credibility, and factors related to
product, user, 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 IS practitioners
concerning the effective design and development of RAs.