The role of attributes in product quality comparisons

Conference Paper (2020)
Author(s)

Felipe Moraes (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Jie Yang (Amazon)

Rongting Zhang (Amazon)

Vanessa Murdock (Amazon)

Research Group
Web Information Systems
DOI related publication
https://doi.org/10.1145/3343413.3377956 Final published version
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Publication Year
2020
Language
English
Research Group
Web Information Systems
Pages (from-to)
253-262
ISBN (electronic)
978-1-4503-6892-6
Event
5th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2020 (2020-03-14 - 2020-03-18), Vancouver, Canada
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Abstract

In online shopping quality is a key consideration when purchasing an item. Since customers cannot physically touch or try out an item before buying it, they must assess its quality from information gathered online. In a typical eCommerce setting, the customer is presented with seller-generated content from the product catalog, such as an image of the product, a textual description, and lists or comparisons of attributes. In addition to catalog attributes, customers often have access to customer-generated content such as reviews and product questions and answers. In a crowdsourced study, we asked crowd workers to compare product pairs from kitchen, electronics, home, beauty and office categories. In a side-by-side comparison, we asked them to choose the product that is higher quality, and further to identify the attributes that contributed to their judgment, where the attributes were both seller-generated and customer-generated. We find that customers tend to perceive more expensive items as higher quality but that their purchase decisions are uncorrelated with quality, suggesting that customers seek a trade-off between price and quality when making purchase decisions. Crowd workers placed a higher value on attributes derived from customer-generated content such as reviews than on catalog attributes. Among the catalog attributes, brand, item material and pack size were most often selected. Finally, attributes with a low correlation with perceived quality are nonetheless useful in predicting purchases in a machine-learned system.

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