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. 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. 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.
    7. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    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. Tadle, Raul Cruz, 2022. "FOMC minutes sentiments and their impact on financial markets," Journal of Economics and Business, Elsevier, vol. 118(C).
    2. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    3. 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.
    4. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    5. Christopher N. Avery & Judith A. Chevalier & Richard J. Zeckhauser, 2016. "The "CAPS" Prediction System and Stock Market Returns," Review of Finance, European Finance Association, vol. 20(4), pages 1363-1381.
    6. Femg, Xunan & Johansson, Anders C., 2019. "News or Noise? The Information Content of Social Media in China," Stockholm School of Economics Asia Working Paper Series 2019-52, Stockholm School of Economics, Stockholm China Economic Research Institute.
    7. André Betzer & Jan Philipp Harries, 2022. "How online discussion board activity affects stock trading: the case of GameStop," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(4), pages 443-472, December.
    8. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    9. 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).
    10. Paolo Cremonesi & Chiara Francalanci & Alessandro Poli & Roberto Pagano & Luca Mazzoni & Alberto Maggioni & Mehdi Elahi, 2018. "Social Network based Short-Term Stock Trading System," Papers 1801.05295, arXiv.org.
    11. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    12. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    13. Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
    14. Yongqiang Meng & Dehua Shen & Xiong Xiong & Jorgen Vitting Andersen, 2020. "A Socio-Finance Model: The Case of Bitcoin," Documents de travail du Centre d'Economie de la Sorbonne 20031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    15. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    16. Niccolò Casnici & Pierpaolo Dondio & Roberto Casarin & Flaminio Squazzoni, 2015. "Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    17. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    18. Ying Zhang & Peggy Swanson, 2010. "Are day traders bias free?—evidence from internet stock message boards," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(1), pages 96-112, January.
    19. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    20. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.

    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.