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Track 1.2 - The Data Science of Startups

Prof Jean-Michel DALLE, Sorbonne Université & Ecole Polytechnique
Prof Matthijs DEN BESTEN, Montpellier Business School


Track's Contacts : 

jean-michel.dalle[AT]sorbonne-universite.fr
m.denbesten[AT]Montpellier-BS.com


In recent years, several open startup databases have emerged, following the creation of Crunchbase in 2007, now one of the leading sources of information about the entrepreneurial ecosystem (Dalle, den Besten & Menon, 2017). Such databases contain detailed information on startups from all over the world, ranging from recording the identity of a company’s founders to information concerning the startups’ funding rounds or textual description of its activity. Typically, Crunchbase currently contains information about over 670 000 organizations, 780 000 individuals, 220 000 funding rounds, 92 000 investors and over 6.3 million pieces of news relating to the entrepreneurial ecosystem worldwide (in the US, 52 000 organizations, 261 000 individuals, 110 000 funding rounds, and 29 000 investors).


These new datasets have first been used by entrepreneurs and investors, but in recent years they have rapidly gained recognition in the academic world, all the more so as they allow researchers to benefit from the methods, tools and techniques associated with Data Science. Liang et al. (2016) and Sharchilev (2018) have for instance worked on predictors of startup funding or success, Gastaud et al. (2018) have developed innovative visualization techniques and metrics for innovative ecosystems, Bhamidipaty and a team of researchers at IBM (2018) have addressed the task of textual similarity matching, while DeSantola et al. (2017) and Ratzinger et al. (2018) have studied financial milestones, in relation respectively to the first female board member and the education of startup founders.


These studies are further in line with the recent increase in cross-fertilization between several disciplines associated with Data Science. They create a context where management sciences can benefit from tools developed in computer science, statistics or physics, in order to study various complex phenomena associated with innovation, R&D and entrepreneurship, typically issues related to startup funding, investor networks, temporal evolutions, etc.


In addition, the growing amount of data on startups and innovative ecosystems available has also become of special relevance for decision-makers, who expect these new endeavours to help them address the innovation challenge, and who have signaled their interest with respect to the exploitation of the new datasets in order to better inform innovation policies.

In this context, this track will welcome contributions and bring together researchers working on these topics and all the matters relevant for the new and emerging "data science of startups"


References

Bhamidipaty, A., et al. (2018), "Towards a Generalized Similarity Service.", KDD’18, August 20th, 2018, London, United Kingdom.
Dalle, J.-M., den Besten, M., & C. Menon (2017), "Using Crunchbase for economic and managerial research", OECD Science, Technology and Industry Working Papers, No. 2017/08, OECD Publishing, Paris, https://doi.org/10.1787/6c418d60-en
DeSantola, A., Ramarajan, L., & Battilana, J. (2017). New Venture Milestones and the First Female Board Member. In Academy of Management Proceedings (Vol. 2017, No. 1, p. 16540). Briarcliff Manor, NY 10510: Academy of Management.
Gastaud, C., Lacroix, T., Taub, R., Dion, G. & Dalle, J.-M. (2018). “Visualizing startup ecosystems and characterizing their diversity”, Proceedings of R&D Management 2018.
Liang Y. E., and Yuan S.-T. D. (2016), "Predicting investor funding behavior using crunchbase social network features", Internet Research, Vol. 26 Issue: 1, pp.74-100, https://doi.org/10.1108/IntR-09-2014-0231
Ratzinger, D., Amess, K., Greenman, A., & Mosey, S. (2018). The impact of digital start-up founders’ higher education on reaching equity investment milestones. The Journal of Technology Transfer, 43(3), 760-778.
Sharchilev, B., Roizner, M., Rumyantsev, A., Ozornin, D., Serdyukov, P., & de Rijke, M. (2018, October). Web-based Startup Success Prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 2283-2291). ACM.