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A multi-facet item response theory approach to improve customer satisfaction using online product ratings

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  • Ling Peng

    (Lingnan University)

  • Geng Cui

    (Lingnan University)

  • Yuho Chung

    (Lingnan University)

  • Chunyu Li

    (Guangdong University of Foreign Studies)

Abstract

While online platforms often provide a single composite rating and the ratings of different attributes of a product, they largely ignore the attribute characteristics and customer criticality, which limits managerial action. We propose a multi-facet item response theory (MFIRT) approach to simultaneously examine the effects of product attributes, reviewer criticality, consumption situation, product type, and time in assessing latent customer satisfaction. Analyses of hotel ratings from TripAdvisor and beer ratings from BeerAdvocate suggest that product attributes differ with respect to their discriminating and threshold characteristics and that reviewer segments emphasize different attributes when rating various products over time. The MFIRT approach predicts product performance more accurately than alternative methods and provides novel insights to inform marketing strategies. The MFIRT framework can fundamentally advance how we analyze customer satisfaction and other consumer attitudes and improve marketing research and practice.

Suggested Citation

  • Ling Peng & Geng Cui & Yuho Chung & Chunyu Li, 2019. "A multi-facet item response theory approach to improve customer satisfaction using online product ratings," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 960-976, September.
    • Handle: RePEc:spr:joamsc:v:47:y:2019:i:5:d:10.1007_s11747-019-00662-w
    DOI: 10.1007/s11747-019-00662-w
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    3. Ruan, Yanya & Mezei, József, 2022. "When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Muhammad Nazmul Hoque & Muhammad Khalilur Rahman & Jamaliah Said & Farhana Begum & Mohammad Mainul Hossain, 2022. "What Factors Influence Customer Attitudes and Mindsets towards the Use of Services and Products of Islamic Banks in Bangladesh?," Sustainability, MDPI, vol. 14(8), pages 1-19, April.
    5. Yuho Chung & Yiwei Li & Jianmin Jia, 2021. "Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 925-946, September.
    6. Agag, Gomaa & Durrani, Baseer Ali & Shehawy, Yasser Moustafa & Alharthi, Majed & Alamoudi, Hawazen & El-Halaby, Sherif & Hassanein, Ahmed & Abdelmoety, Ziad H., 2023. "Understanding the link between customer feedback metrics and firm performance," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    7. Jérôme Baray & Gérard Cliquet, 2025. "AI‐Driven Sentiment Analysis for Retail Management: A Graph‐Based DSS Comparing Franchise and Company‐Owned Stores," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(4), pages 2345-2363, June.

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