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Understanding customer regional differences from online opinions: a hierarchical Bayesian approach

Author

Listed:
  • Kejia Chen

    (Fuzhou University)

  • Jian Jin

    (Beijing Normal University)

  • Zheng Zhao

    (Fuzhou University)

  • Ping Ji

    (The Hong Kong Polytechnic University)

Abstract

A large volume of customer reviews is generated from time to time and customer requirements are presented between lines of online opinions. Many studies about online opinions mainly focus on the extraction of customer sentiment, but practical concerns regarding the integration into new product design are far from extensively discussed. To enlighten designers about how consumers differ geographically in terms of their preferences, which is possessing important research significance and practical values, is not well investigated. Specifically, in this study, online reviews are invited to explore market regional heterogeneity. With identified product feature related subjective sentences from online reviews, a straightforward applied approach is to assume the ratio of the number of satisfied customers to the total number of customers as the expected percentage of satisfied customers across different regions. However, such frequency based approach becomes unreliable in case that the number of reviews do not distribute evenly. Accordingly, the Bayesian school of thought is utilized in which statistics of data-rich regions are invited to help to analyze that of data-poor regions. Then, a hierarchical Bayesian model is proposed and it assumes that the expected percentages of customer satisfaction in different regions follow a certain probability distribution. Finally, taking 9541 mobile phone online reviews on Amazon as an example, categories of experiments were conducted. It informs the significance to product designers about the value of online concerns on analyzing market regional heterogeneity and presents the effectiveness of the proposed approach in terms of discovering customer regional differences.

Suggested Citation

  • Kejia Chen & Jian Jin & Zheng Zhao & Ping Ji, 2022. "Understanding customer regional differences from online opinions: a hierarchical Bayesian approach," Electronic Commerce Research, Springer, vol. 22(2), pages 377-403, June.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:2:d:10.1007_s10660-020-09420-5
    DOI: 10.1007/s10660-020-09420-5
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    References listed on IDEAS

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    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Udo, Godwin J. & Bagchi, Kallol K. & Kirs, Peeter J., 2010. "An assessment of customers’ e-service quality perception, satisfaction and intention," International Journal of Information Management, Elsevier, vol. 30(6), pages 481-492.
    3. Jian Jin & Ying Liu & Ping Ji & Hongguang Liu, 2016. "Understanding big consumer opinion data for market-driven product design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 3019-3041, May.
    4. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    5. Dong Wang & Jiexun Li & Kaiquan Xu & Yizhen Wu, 2017. "Sentiment community detection: exploring sentiments and relationships in social networks," Electronic Commerce Research, Springer, vol. 17(1), pages 103-132, March.
    6. Xu, Xun & Wang, Xuequn & Li, Yibai & Haghighi, Mohammad, 2017. "Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors," International Journal of Information Management, Elsevier, vol. 37(6), pages 673-683.
    7. Jin Cao & Zhibin Jiang & Kangzhou Wang, 2016. "Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction," International Journal of Production Research, Taylor & Francis Journals, vol. 54(5), pages 1303-1321, March.
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