IDEAS home Printed from https://ideas.repec.org/a/eee/jobuve/v11y2019ic4.html
   My bibliography  Save this article

Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy

Author

Listed:
  • Antretter, Torben
  • Blohm, Ivo
  • Grichnik, Dietmar
  • Wincent, Joakim

Abstract

Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures’ Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.

Suggested Citation

  • Antretter, Torben & Blohm, Ivo & Grichnik, Dietmar & Wincent, Joakim, 2019. "Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy," Journal of Business Venturing Insights, Elsevier, vol. 11(C), pages 1-1.
    • Handle: RePEc:eee:jobuve:v:11:y:2019:i:c:4
    DOI: 10.1016/j.jbvi.2018.e00109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2352673418301197
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbvi.2018.e00109?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. Malyy, Maksim & Tekic, Zeljko & Podladchikova, Tatiana, 2021. "The value of big data for analyzing growth dynamics of technology-based new ventures," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Seigner, Benedikt David Christian & Milanov, Hana & Lundmark, Erik & Shepherd, Dean A., 2023. "Tweeting like Elon? Provocative language, new-venture status, and audience engagement on social media," Journal of Business Venturing, Elsevier, vol. 38(2).
    3. Khanindra Ch. Das, 2023. "What Affects Startup Acquisition in Emerging Economy? Evidence from India," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 22(2), pages 111-134, June.
    4. Tumasjan, Andranik & Braun, Reiner & Stolz, Barbara, 2021. "Twitter sentiment as a weak signal in venture capital financing," Journal of Business Venturing, Elsevier, vol. 36(2).
    5. Lucas, David S. & Park, U. David, 2023. "The nature and origins of social venture mission: An exploratory study of political ideology and moral foundations," Journal of Business Venturing, Elsevier, vol. 38(2).
    6. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    7. Muhammad Sualeh Khattak & Muhammad Anwar & Thomas Clauß, 2021. "The Role of Entrepreneurial Finance in Corporate Social Responsibility and New Venture Performance in an Emerging Market," Journal of Entrepreneurship and Innovation in Emerging Economies, Entrepreneurship Development Institute of India, vol. 30(2), pages 336-366, September.
    8. Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023. "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers 2309.15552, arXiv.org.
    9. Jung, Sang Hoon & Jeong, Yong Jin, 2020. "Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level," Technology in Society, Elsevier, vol. 63(C).
    10. Ungerer, Christina & Reuther, Kevin & Baltes, Guido, 2021. "The lingering living dead phenomenon: Distorting venture survival studies?," Journal of Business Venturing Insights, Elsevier, vol. 16(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:eee:jobuve:v:11:y:2019:i:c:4. 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: https://www.journals.elsevier.com/journal-of-business-venturing-insights .

    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.