Predicting new venture gestation outcomes with machine learning methods

Koumbarakis, Paris; Volery, Thierry

doi:10.1080/00472778.2022.2082453

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25478

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Predicting new venture gestation outcomes with machine learning methods
Authors:	Koumbarakis, Paris Volery, Thierry
et. al:	No
DOI:	10.1080/00472778.2022.2082453 10.21256/zhaw-25478
Published in:	Journal of Small Business Management
Volume(Issue):	61
Issue:	5
Page(s):	2227
Pages to:	2260
Issue Date:	15-Jun-2022
Publisher / Ed. Institution:	Wiley
ISSN:	0047-2778 1540-627X
Language:	English
Subjects:	Forecasting; Machine learning; New venture creation
Subject (DDC):	006: Special computer methods 658.1: Organization and finance
Abstract:	This study explores the use of machine learning methods to forecast the likelihood of firm birth and firm abandonment during the first five years of a new business gestation. The predictability of traditional logistic regression is compared with several machine learning methods, including logistic regression, k-nearest neighbors, random forest, extreme gradient boosting, support vector machines, and artificial neural networks. While extreme gradient boosting shows the best overall model performance, neural networks provide good results by correctly classifying entrepreneurs who have not abandoned their business venture in the early stage of the gestation process. In addition, this study provides valuable insights in relation to the start-up activities leading to firm emergence. Entrepreneurs who perform a greater number of activities and who can orchestrate them at the right rate, concentration, and time are more likely to successfully launch a new business venture.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25478
Fulltext version:	Published version
License (according to publishing contract):	CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement:	School of Management and Law
Organisational Unit:	Institute of Innovation and Entrepreneurship (IIE)
Appears in collections:	Publikationen School of Management and Law

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Koumbarakis, P., & Volery, T. (2022). Predicting new venture gestation outcomes with machine learning methods. Journal of Small Business Management, 61(5), 2227–2260. https://doi.org/10.1080/00472778.2022.2082453

Koumbarakis, P. and Volery, T. (2022) ‘Predicting new venture gestation outcomes with machine learning methods’, Journal of Small Business Management, 61(5), pp. 2227–2260. Available at: https://doi.org/10.1080/00472778.2022.2082453.

P. Koumbarakis and T. Volery, “Predicting new venture gestation outcomes with machine learning methods,” Journal of Small Business Management, vol. 61, no. 5, pp. 2227–2260, Jun. 2022, doi: 10.1080/00472778.2022.2082453.

KOUMBARAKIS, Paris und Thierry VOLERY, 2022. Predicting new venture gestation outcomes with machine learning methods. Journal of Small Business Management. 15 Juni 2022. Bd. 61, Nr. 5, S. 2227–2260. DOI 10.1080/00472778.2022.2082453

Koumbarakis, Paris, and Thierry Volery. 2022. “Predicting New Venture Gestation Outcomes with Machine Learning Methods.” Journal of Small Business Management 61 (5): 2227–60. https://doi.org/10.1080/00472778.2022.2082453.

Koumbarakis, Paris, and Thierry Volery. “Predicting New Venture Gestation Outcomes with Machine Learning Methods.” Journal of Small Business Management, vol. 61, no. 5, June 2022, pp. 2227–60, https://doi.org/10.1080/00472778.2022.2082453.