TY - GEN
T1 - Helping Voice Shoppers Make Purchase Decisions
AU - Penha, Gustavo
AU - Krikon, Eyal
AU - Murdock, Vanessa
AU - Avula, Sandeep
PY - 2022
Y1 - 2022
N2 - Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.
AB - Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.
UR - http://www.scopus.com/inward/record.url?scp=85129726870&partnerID=8YFLogxK
U2 - 10.1145/3491101.3519828
DO - 10.1145/3491101.3519828
M3 - Conference contribution
AN - SCOPUS:85129726870
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery (ACM)
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
Y2 - 30 April 2022 through 5 May 2022
ER -