Improving the Communication Interfaces between Consumers and Online product Recommendation Agents

Research output: Faculty's ThesesDoctoral thesis

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Original languageEnglish
Awarding Institution
  • University of British Columbia
  • Benbasat, Izak, Supervisor, External person
  • Cenfetelli, Ron, Supervisor, External person
Publication statusPublished - Aug 2011
Externally publishedYes


An online recommendation agent (RA) provides users assistance by eliciting from users their product preferences and then recommending products that satisfy these preferences. While the importance of the RA has been emphasized by practitioners and scholars, precisely how to implement the RA, and what an RA’s effectiveness is relative to other recommendation sources, are not well understood. Through three empirical studies conducted utilizing the experimental method, this dissertation evaluates and improves the input, process, and output interfaces of an RA to facilitate the communication between consumers and RAs in order to reduce decision effort and enhance the quality of their purchasing decisions.
Regarding the input component of an RA, Study 1 finds that an RA that interactively demonstrates trade-offs among product attributes improves consumers’ perceived enjoyment and perceived product diagnosticity. It also finds that a medium level of trade-off transparency should be revealed to the user, as it leads to the best perceived enjoyment and product diagnosticity. Further, Study 1 augments the Effort-Accuracy Framework by proposing perceived enjoyment and perceived product diagnosticity as two antecedents for decision quality and decision effort.
With respect to the process component of an RA, Study 2 evaluates the efficacy of three types of user feedback (attribute-based feedback, alternative-based feedback, and integrated feedback) in an e-commerce setting and shows that they are better than the absence of feedback in terms of perceived decision effort. Additionally, Study 2 demonstrates that the recommendation source (RA, consumers, or experts) moderates the effects of the three types of user feedback on perceived decision quality.
Regarding the output component, Study 3 shows that users are more likely to select a product that is commonly recommended by multiple sources. This also results in higher perceived decision quality. Study 3 also reveals that users with high product knowledge or task involvement are more likely to adhere to the recommendation from the RA as compared to recommendations from experts or consumers. Further, users who rely on the RA’s recommendations will perceive a higher level of decision quality as compared to those who rely on consumer or expert recommendations.