Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms - Masoumeh Moterased; Seyed Mojtaba Sajadi; Ali Davari; Mohammad Reza Zali (Big Data and Computing Visions)
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.