IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0123129.html
   My bibliography  Save this article

Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao

Author

Listed:
  • Qingguo Ma
  • Wuke Zhang

Abstract

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the “Diaoyu Island” event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products.

Suggested Citation

  • Qingguo Ma & Wuke Zhang, 2015. "Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0123129
    DOI: 10.1371/journal.pone.0123129
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123129
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0123129&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0123129?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
    ---><---

    References listed on IDEAS

    as
    1. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    2. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. Hong-Hee Won & Woojae Myung & Gil-Young Song & Won-Hee Lee & Jong-Won Kim & Bernard J Carroll & Doh Kwan Kim, 2013. "Predicting National Suicide Numbers with Social Media Data," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    4. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
    2. Gaku Fukunaga & Hideki Takayasu & Misako Takayasu, 2016. "Property of Fluctuations of Sales Quantities by Product Category in Convenience Stores," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.

    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. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
    2. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    3. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    4. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.
    5. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2019. "Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions," Papers 1909.03792, arXiv.org, revised Sep 2019.
    6. Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
    7. Livio Fenga, 2020. "Filtering and prediction of noisy and unstable signals: The case of Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 281-295, March.
    8. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2020. "Tehran stock exchange prediction using sentiment analysis of online textual opinions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 22-37, January.
    9. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
    10. Mioara, POPESCU, 2015. "Construction Of Economic Indicators Using Internet Searches," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 6(1), pages 25-31.
    11. Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," CEBI working paper series 21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    12. Tommaso Colussi & Ingo E. Isphording & Nico Pestel, 2021. "Minority Salience and Political Extremism," American Economic Journal: Applied Economics, American Economic Association, vol. 13(3), pages 237-271, July.
    13. Kučerová, Zuzana & Pakši, Daniel & Koňařík, Vojtěch, 2024. "Macroeconomic fundamentals and attention: What drives european consumers’ inflation expectations?," Economic Systems, Elsevier, vol. 48(1).
    14. David W Carter & Scott Crosson & Christopher Liese, 2015. "Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-18, September.
    15. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    16. C. Douglas Swearingen & Joseph T. Ripberger, 2014. "Google Insights and U.S. Senate Elections: Does Search Traffic Provide a Valid Measure of Public Attention to Political Candidates?," Social Science Quarterly, Southwestern Social Science Association, vol. 95(3), pages 882-893, September.
    17. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    18. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    19. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    20. Sansone, Dario, 2019. "Pink work: Same-sex marriage, employment and discrimination," Journal of Public Economics, Elsevier, vol. 180(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0123129. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.