Reducing the Perceived Deception of Product Recommendation Agents: The Impact of Perceived Verifiability and Perceived Similarity

Bo Xiao, Chee-Wee Tan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Product Recommendations Agents (PRAs) are software applications that augment consumers' purchasing decisions by offering product recommendations based on elicited customers' preferences. The underlying premise of PRAs is often grounded on the assumption that PRAs seek to optimize consumers' utility by tailoring product recommendations to meet requisite expectations. Because the majority of commercial PRAs are implemented by parties with partisan interests in product sales, it is highly probable that recommendations are biased in favor of their providers and do not accurately reflect consumers' interests. This in turn may possibly induce perceptions of deception among consumers. This study theorizes that the incorporation of IT-mediated components in PRAs, which induce high levels of perceived verifiability and perceived similarity, could mitigate consumers' perceptions of deception towards product recommendations.
Product Recommendations Agents (PRAs) are software applications that augment consumers' purchasing decisions by offering product recommendations based on elicited customers' preferences. The underlying premise of PRAs is often grounded on the assumption that PRAs seek to optimize consumers' utility by tailoring product recommendations to meet requisite expectations. Because the majority of commercial PRAs are implemented by parties with partisan interests in product sales, it is highly probable that recommendations are biased in favor of their providers and do not accurately reflect consumers' interests. This in turn may possibly induce perceptions of deception among consumers. This study theorizes that the incorporation of IT-mediated components in PRAs, which induce high levels of perceived verifiability and perceived similarity, could mitigate consumers' perceptions of deception towards product recommendations.
LanguageDanish
Title of host publicationProceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
Date2006
Pages75-79
StatePublished - 2006
Externally publishedYes
EventThe Fifth Annual Pre-ICIS Workshop on HCI Research in MIS
- Hilton Regency Ballroom, Milwaukee, United States
Duration: 10 Dec 200613 Dec 2006
Conference number: 5
http://sighci.org/index.php?page=pre-icis-2006&phpMyAdmin=622c4d141843t506dba2f

Conference

ConferenceThe Fifth Annual Pre-ICIS Workshop on HCI Research in MIS
Number5
LocationHilton Regency Ballroom
CountryUnited States
CityMilwaukee
Period10/12/200613/12/2006
SponsorAssociation for Information Systems Special Interest Group on Human Computer Interaction
Internet address

Bibliographical note

CBS Library does not have access to the material

Keywords

    Cite this

    Xiao, B., & Tan, C-W. (2006). Reducing the Perceived Deception of Product Recommendation Agents: The Impact of Perceived Verifiability and Perceived Similarity. In Proceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006 (pp. 75-79). Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL).
    Xiao, Bo ; Tan, Chee-Wee. / Reducing the Perceived Deception of Product Recommendation Agents : The Impact of Perceived Verifiability and Perceived Similarity. Proceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2006. pp. 75-79
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    title = "Reducing the Perceived Deception of Product Recommendation Agents: The Impact of Perceived Verifiability and Perceived Similarity",
    abstract = "Product Recommendations Agents (PRAs) are software applications that augment consumers' purchasing decisions by offering product recommendations based on elicited customers' preferences. The underlying premise of PRAs is often grounded on the assumption that PRAs seek to optimize consumers' utility by tailoring product recommendations to meet requisite expectations. Because the majority of commercial PRAs are implemented by parties with partisan interests in product sales, it is highly probable that recommendations are biased in favor of their providers and do not accurately reflect consumers' interests. This in turn may possibly induce perceptions of deception among consumers. This study theorizes that the incorporation of IT-mediated components in PRAs, which induce high levels of perceived verifiability and perceived similarity, could mitigate consumers' perceptions of deception towards product recommendations.",
    keywords = "Perceived deception, Product recommendation, Perceived verifiability, Perceived similarity",
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    Xiao, B & Tan, C-W 2006, Reducing the Perceived Deception of Product Recommendation Agents: The Impact of Perceived Verifiability and Perceived Similarity. in Proceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006. Association for Information Systems. AIS Electronic Library (AISeL), Atlanta, GA, pp. 75-79, Milwaukee, United States, 10/12/2006.

    Reducing the Perceived Deception of Product Recommendation Agents : The Impact of Perceived Verifiability and Perceived Similarity. / Xiao, Bo; Tan, Chee-Wee.

    Proceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2006. p. 75-79.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    Xiao B, Tan C-W. Reducing the Perceived Deception of Product Recommendation Agents: The Impact of Perceived Verifiability and Perceived Similarity. In Proceedings of the Fifth annual Workshop on HCI Research in MIS, Milwaukee, WI. December 9, 2006. Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). 2006. p. 75-79.