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 |
Files in This Item:
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2022_Koumbarakis-Volery_Predicting-new-venture-gestation-outcomes-machine-learning.pdf | 2.12 MB | Adobe PDF | View/Open |
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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.
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