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The Role of Attributes in Product Quality Comparisons

Published: 14 March 2020 Publication History

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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  • (2022)Helping Voice Shoppers Make Purchase DecisionsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519828(1-8)Online publication date: 27-Apr-2022
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cover image ACM Conferences
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
March 2020
596 pages
ISBN:9781450368926
DOI:10.1145/3343413
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 March 2020

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Author Tags

  1. attribute comparison
  2. online reviews
  3. product quality

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  • (2023)Mitigating the risk induced by Online shopping by using Metaverse2023 International Seminar on Application for Technology of Information and Communication (iSemantic)10.1109/iSemantic59612.2023.10295282(129-134)Online publication date: 16-Sep-2023
  • (2022)Classifier Construction Under Budget ConstraintsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517863(1160-1174)Online publication date: 10-Jun-2022
  • (2022)Helping Voice Shoppers Make Purchase DecisionsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519828(1-8)Online publication date: 27-Apr-2022
  • (2021)Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product SearchBig Data and Cognitive Computing10.3390/bdcc50300355:3(35)Online publication date: 13-Aug-2021
  • (2021)EX3: Explainable Attribute-aware Item-set RecommendationsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474240(484-494)Online publication date: 13-Sep-2021
  • (2021)Challenges and research opportunities in eCommerce search and recommendationsACM SIGIR Forum10.1145/3451964.345196654:1(1-23)Online publication date: 19-Feb-2021
  • (2021)Dataset of Natural Language Queries for E-CommerceProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446043(307-311)Online publication date: 14-Mar-2021
  • (2020)CC-News-EnProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412762(3077-3084)Online publication date: 19-Oct-2020

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