IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v18y2018i2d10.1007_s10660-017-9259-6.html
   My bibliography  Save this article

Game theoretic approach of a novel decision policy for customers based on big data

Author

Listed:
  • Shasha Liu

    (Chongqing University)

  • Bingjia Shao

    (Chongqing University)

  • Yuan Gao

    (Xichang Satellite Launch Center
    China Defense Science and Technology Information Center
    Tsinghua University)

  • Su Hu

    (University of Electronic Science and Technology of China)

  • Yi Li

    (The High School Affiliated to Renmin University of China)

  • Weigui Zhou

    (Xichang Satellite Launch Center)

Abstract

In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.

Suggested Citation

  • Shasha Liu & Bingjia Shao & Yuan Gao & Su Hu & Yi Li & Weigui Zhou, 2018. "Game theoretic approach of a novel decision policy for customers based on big data," Electronic Commerce Research, Springer, vol. 18(2), pages 225-240, June.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:2:d:10.1007_s10660-017-9259-6
    DOI: 10.1007/s10660-017-9259-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-017-9259-6
    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/s10660-017-9259-6?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. Mike Bennett, 2013. "The financial industry business ontology: Best practice for big data," Journal of Banking Regulation, Palgrave Macmillan, vol. 14(3-4), pages 255-268, July.
    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. Hans Weytjens & Enrico Lohmann & Martin Kleinsteuber, 2021. "Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet," Electronic Commerce Research, Springer, vol. 21(2), pages 371-391, June.
    2. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.

    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. Caterina Pietra & Roberto De Lotto & Rakan Bahshwan, 2021. "Approaching Healthy City Ontology: First-Level Classes Definition Using BFO," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    2. Bell Raj Eapen & Vishwanath V. Baba & Maarif Sohail, 2023. "Evidence-Based Management: A Design Theory, Template, and Technology for a Knowledge Delivery Platform," Metamorphosis: A Journal of Management Research, , vol. 22(1), pages 73-84, June.
    3. Bholat, David, 2016. "Modelling metadata in central banks," Statistics Paper Series 13, European Central Bank.
    4. Maria V. Sigova & Igor K. Klyuchnikov & Oleg I. Klyuchnikov, 2024. "Sustainability and Security of Green Finance from the Multi-agent Games Perspective," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 78-95, February.
    5. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    6. Frizzo-Barker, Julie & Chow-White, Peter A. & Mozafari, Maryam & Ha, Dung, 2016. "An empirical study of the rise of big data in business scholarship," International Journal of Information Management, Elsevier, vol. 36(3), pages 403-413.

    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:18:y:2018:i:2:d:10.1007_s10660-017-9259-6. 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.