IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v106y2016icp37-44.html
   My bibliography  Save this article

Application of the grey Lotka–Volterra model to forecast the diffusion and competition analysis of the TV and smartphone industries

Author

Listed:
  • Wang, Hsi-Tse
  • Wang, Ta-Chung

Abstract

To achieve competitive advantages, companies need to embrace changes and evolve strategies for coping with challenges when time and data are limited. This study applies grey forecast theory with the Lotka–Volterra competition model to explore the dynamic competition between smart TVs and flat panel TVs, as well as Android and iOS smartphone operating systems (OS). The results show the growing strength of smart TVs and the Android OS is superior to other competing products. With respect to the interactive relationship between products, the two aforementioned products represent the competition relationship of predators and prey: flat panel TVs and iOS are playing the role of prey, while smart TVs and Android are the predators. After comparing forecast accuracy among the model proposed in this study, the grey forecasting model GM(1,1), and Lotka–Volterra model, we found the proposed model has the best accuracy. Companies can use the proposed model to develop a strategic plan feasible enough to secure a sustainable competitive advantage.

Suggested Citation

  • Wang, Hsi-Tse & Wang, Ta-Chung, 2016. "Application of the grey Lotka–Volterra model to forecast the diffusion and competition analysis of the TV and smartphone industries," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 37-44.
    • Handle: RePEc:eee:tefoso:v:106:y:2016:i:c:p:37-44
    DOI: 10.1016/j.techfore.2016.02.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162516000494
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2016.02.008?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.

    Citations

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


    Cited by:

    1. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Gatabazi, P. & Mba, J.C. & Pindza, E., 2019. "Modeling cryptocurrencies transaction counts using variable-order Fractional Grey Lotka-Volterra dynamical system," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 283-290.
    3. Yang, Chunyu & Huang, Jue & Lin, Zhibin & Zhang, Danxia & Zhu, Ying & Xu, Xinghua & Chen, Mei, 2018. "Evaluating the symbiosis status of tourist towns: The case of Guizhou Province, China," Annals of Tourism Research, Elsevier, vol. 72(C), pages 109-125.
    4. Dingxuan Huang & Claudio O. Delang & Yongjiao Wu & Shuliang Li, 2021. "An Improved Lotka–Volterra Model Using Quantum Game Theory," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    5. Gatabazi, P. & Mba, J.C. & Pindza, E. & Labuschagne, C., 2019. "Grey Lotka–Volterra models with application to cryptocurrencies adoption," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 47-57.
    6. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.

    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:eee:tefoso:v:106:y:2016:i:c:p:37-44. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.