Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms
This study discusses the prediction model of Entrepreneurial Exit from Entrepreneurial Perceptions, acquired the data from the Global Entrepreneurship Monitor's (GEM) database in 2008-2019. Some essential indicators include Opportunity Perception, Fear of Failure, Capability Perception, Role Mo...
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| Format: | Article |
| Language: | English |
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REA Press
2021-09-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_142089_8a16ac30220adec3126f8bad2979b8fc.pdf |
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| author | Masoumeh Moterased Seyed Mojtaba Sajadi Ali Davari Mohammad Reza Zali |
| author_facet | Masoumeh Moterased Seyed Mojtaba Sajadi Ali Davari Mohammad Reza Zali |
| author_sort | Masoumeh Moterased |
| collection | DOAJ |
| description | This study discusses the prediction model of Entrepreneurial Exit from Entrepreneurial Perceptions, acquired the data from the Global Entrepreneurship Monitor's (GEM) database in 2008-2019. Some essential indicators include Opportunity Perception, Fear of Failure, Capability Perception, Role Model, and Entrepreneurial Intention. Data mining results show that the exit reasons and entrepreneurial intention have a more significant impact on entrepreneurial exit than other variables. This research applies the Random Forest Algorithm to get a prediction model that shows the entrepreneurial exit. According to the Random Forest Algorithm results, accuracy, ROC-AUC score, AUC curve, precision, recall, and F1 score validate the classification method. The prediction model shows that the best accuracy predictor of entrepreneurial exit is 99 percent, and another criteria ROC_AUC score 96%. Consistent results demonstrate that the proposed method can consider a promisingly successful predictive model of entrepreneurial exit with excellent predictive performance. These results can predict the individuals' entrepreneurial exit possibility before the psychological and financial impact and loss of capital and failure. |
| format | Article |
| id | doaj-art-ed74b2582c2b48ae876601f20e9b1166 |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2021-09-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-ed74b2582c2b48ae876601f20e9b11662025-01-30T12:21:25ZengREA PressBig Data and Computing Visions2783-49562821-014X2021-09-011311112710.22105/bdcv.2021.142089142089Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithmsMasoumeh Moterased0Seyed Mojtaba Sajadi1Ali Davari2Mohammad Reza Zali3Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.School of Strategy and Leadership, Faculty of Business and law, Coventry University, UK.Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.This study discusses the prediction model of Entrepreneurial Exit from Entrepreneurial Perceptions, acquired the data from the Global Entrepreneurship Monitor's (GEM) database in 2008-2019. Some essential indicators include Opportunity Perception, Fear of Failure, Capability Perception, Role Model, and Entrepreneurial Intention. Data mining results show that the exit reasons and entrepreneurial intention have a more significant impact on entrepreneurial exit than other variables. This research applies the Random Forest Algorithm to get a prediction model that shows the entrepreneurial exit. According to the Random Forest Algorithm results, accuracy, ROC-AUC score, AUC curve, precision, recall, and F1 score validate the classification method. The prediction model shows that the best accuracy predictor of entrepreneurial exit is 99 percent, and another criteria ROC_AUC score 96%. Consistent results demonstrate that the proposed method can consider a promisingly successful predictive model of entrepreneurial exit with excellent predictive performance. These results can predict the individuals' entrepreneurial exit possibility before the psychological and financial impact and loss of capital and failure.https://www.bidacv.com/article_142089_8a16ac30220adec3126f8bad2979b8fc.pdfentrepreneurial exitentrepreneurial perceptionsmachine learningglobal entrepreneurship monitor (gem) |
| spellingShingle | Masoumeh Moterased Seyed Mojtaba Sajadi Ali Davari Mohammad Reza Zali Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms Big Data and Computing Visions entrepreneurial exit entrepreneurial perceptions machine learning global entrepreneurship monitor (gem) |
| title | Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms |
| title_full | Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms |
| title_fullStr | Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms |
| title_full_unstemmed | Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms |
| title_short | Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms |
| title_sort | toward prediction of entrepreneurial exit in iran a study based on gem 2008 2019 data and approach of machine learning algorithms |
| topic | entrepreneurial exit entrepreneurial perceptions machine learning global entrepreneurship monitor (gem) |
| url | https://www.bidacv.com/article_142089_8a16ac30220adec3126f8bad2979b8fc.pdf |
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