IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i2d10.1007_s10660-020-09420-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09420-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10660-020-09420-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    2. Ladi Daodu & Prof. Dr. Amiya Bhaumik, 2024. "Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 77-91, June.
    3. C, Deep Prakash & Majumdar, Adrija, 2023. "Predicting sports fans’ engagement with culturally aligned social media content: A language expectancy perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    4. Xiao, Yan & Li, Congdong & Thürer, Matthias & Liu, Yide & Qu, Ting, 2022. "User preference mining based on fine-grained sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    5. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    6. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    7. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    8. Bindu K. Nambiar & Kartikeya Bolar, 2023. "Factors influencing customer preference of cardless technology over the card for cash withdrawals: an extended technology acceptance model," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 58-73, March.
    9. Uttara Ananthakrishnan & Davide Proserpio & Siddhartha Sharma, 2023. "I Hear You: Does Quality Improve with Customer Voice?," Marketing Science, INFORMS, vol. 42(6), pages 1143-1161, November.
    10. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    11. Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.
    12. Qing Huan & Niu ZhanWen, 2018. "Knowledge management in consultancy involved LPS implementation projects via social media," Electronic Commerce Research, Springer, vol. 18(1), pages 89-107, March.
    13. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
    14. von Hippel, Eric & Kaulartz, Sandro, 2021. "Next-generation consumer innovation search: Identifying early-stage need-solution pairs on the web," Research Policy, Elsevier, vol. 50(8).
    15. Oetzel, Sebastian & Graf, Denise, 2023. "Fragen oder Zuhören? Ein Vergleich von Kundenbefragungen und User Generated Content," PraxisWISSEN Marketing: German Journal of Marketing, AfM – Arbeitsgemeinschaft für Marketing, vol. 8(01/2023), pages 91-107.
    16. Eleanor Kohler & Emmanuel Mogaji & İsmail Erkan, 2023. "Save the Trip to the Store: Sustainable Shopping, Electronic Word of Mouth on Instagram and the Impact on Cosmetic Purchase Intentions," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    17. Symeon Symeonidis & Georgios Peikos & Avi Arampatzis, 2022. "Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool," Operational Research, Springer, vol. 22(5), pages 6007-6036, November.
    18. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    19. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
    20. Hsu, Meng-Hsiang & Chang, Chun-Ming & Chuang, Li-Wen, 2015. "Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan," International Journal of Information Management, Elsevier, vol. 35(1), pages 45-56.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:22:y:2022:i:2:d:10.1007_s10660-020-09420-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.