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Data Science in Strategy: Machine learning and text analysis in the study of firm growth

Author

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  • Daan Kolkman

    (Technical University Eindhoven)

  • Arjen van Witteloostuijn

    (Vrije Universiteit Amsterdam)

Abstract

This study examines the applicability of modern Data Science techniques in the domain of Strategy. We apply novel techniques from the field of machine learning and text analysis. WE proceed in two steps. First, we compare different machine learning techniques to traditional regression methods in terms of their goodness-of-fit, using a dataset with 168,055 firms, only including basic demographic and financial information. The novel methods fare to three to four times better, with the random forest technique achieving the best goodness-of-fit. Second, based on 8,163 informative websites of Dutch SMEs, we construct four additional proxies for personality and strategy variables. Including our four text-analyzed variables adds about 2.5 per cent to the R2. Together, our pair of contributions provide evidence for the large potential of applying modern Data Science techniques in Strategy research. We reflect on the potential contribution of modern Data Science techniques from the perspective of the common critique that machine learning offers increased predictive accuracy at the expense of explanatory insight. Particularly, we will argue and illustrate why and how machine learning can be a productive element in the abductive theory-building cycle.

Suggested Citation

  • Daan Kolkman & Arjen van Witteloostuijn, 2019. "Data Science in Strategy: Machine learning and text analysis in the study of firm growth," Tinbergen Institute Discussion Papers 19-066/VI, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20190066
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    File URL: https://papers.tinbergen.nl/19066.pdf
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    References listed on IDEAS

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    1. Wijbenga, Frits H. & van Witteloostuijn, Arjen, 2007. "Entrepreneurial locus of control and competitive strategies - The moderating effect of environmental dynamism," Journal of Economic Psychology, Elsevier, vol. 28(5), pages 566-589, October.
    2. Elizabeth Garnsey & Erik Stam & Paul Heffernan, 2006. "New Firm Growth: Exploring Processes and Paths," Industry and Innovation, Taylor & Francis Journals, vol. 13(1), pages 1-20.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. Andrew Gelman & Guido Imbens, 2013. "Why ask Why? Forward Causal Inference and Reverse Causal Questions," NBER Working Papers 19614, National Bureau of Economic Research, Inc.
    5. Danny Miller & Jean-Marie Toulouse, 1986. "Chief Executive Personality and Corporate Strategy and Structure in Small Firms," Management Science, INFORMS, vol. 32(11), pages 1389-1409, November.
    6. Christophe Boone & Bert De Brabander & Arjen Van Witteloostuijn, 1996. "Ceo Locus of Control and Small Firm Performance: an Integrative Framework and Empirical Test," Journal of Management Studies, Wiley Blackwell, vol. 33(5), pages 667-700, September.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    2. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.

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    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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