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

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  • 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
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    Cited by:

    1. 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).
    2. 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).
    3. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    4. 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.
    5. Graham, Byron & Bonner, Karen, 2024. "The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach," Journal of Business Research, Elsevier, vol. 175(C).
    6. 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.
    7. 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).
    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. 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).
    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).
    11. 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).

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