{"title":"预测摩洛哥企业家的创业活动:机器学习方法","authors":"Ghizlane Boutaky, Ibtissam Youb, Gerard Dokou Kokou, Karima Mialed, Eric Vernier, Mohamed Hamlich, Sebastián Ventura, Soukaina Boutaky","doi":"10.1177/09504222241266681","DOIUrl":null,"url":null,"abstract":"Entrepreneurial activity, a subject of enduring intrigue among scholars, continues to captivate attention, especially in distinct contexts such as Morocco. This study undertakes the formidable task of comprehending and forecasting entrepreneurial activity using the comprehensive Global Entrepreneurship Monitor (GEM) dataset for Morocco. Employing a diverse range of machine learning classifiers, including logistic regression, random forest, support vector machines, gradient boosting, and K-nearest neighbors, our research excels in predicting entrepreneurial activity with remarkable accuracy. Notably, support vector machines emerge as the most potent classifier, achieving an impressive accuracy rate of 95.33%. These findings transcend the inherent complexities of understanding the infrequent nature of entrepreneurial activity, providing invaluable insights into predictive modelling within the Moroccan entrepreneurial landscape. This research not only advances our comprehension of entrepreneurship but also paves the way for informed policymaking and the nurturing of a thriving entrepreneurial ecosystem. This underscores the critical importance of effective data handling and model refinement in achieving these milestones.","PeriodicalId":46591,"journal":{"name":"Industry and Higher Education","volume":"31 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward predicting entrepreneurial activity among Moroccan Entrepreneurs: A machine learning approach\",\"authors\":\"Ghizlane Boutaky, Ibtissam Youb, Gerard Dokou Kokou, Karima Mialed, Eric Vernier, Mohamed Hamlich, Sebastián Ventura, Soukaina Boutaky\",\"doi\":\"10.1177/09504222241266681\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Entrepreneurial activity, a subject of enduring intrigue among scholars, continues to captivate attention, especially in distinct contexts such as Morocco. This study undertakes the formidable task of comprehending and forecasting entrepreneurial activity using the comprehensive Global Entrepreneurship Monitor (GEM) dataset for Morocco. Employing a diverse range of machine learning classifiers, including logistic regression, random forest, support vector machines, gradient boosting, and K-nearest neighbors, our research excels in predicting entrepreneurial activity with remarkable accuracy. Notably, support vector machines emerge as the most potent classifier, achieving an impressive accuracy rate of 95.33%. These findings transcend the inherent complexities of understanding the infrequent nature of entrepreneurial activity, providing invaluable insights into predictive modelling within the Moroccan entrepreneurial landscape. This research not only advances our comprehension of entrepreneurship but also paves the way for informed policymaking and the nurturing of a thriving entrepreneurial ecosystem. This underscores the critical importance of effective data handling and model refinement in achieving these milestones.\",\"PeriodicalId\":46591,\"journal\":{\"name\":\"Industry and Higher Education\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Industry and Higher Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09504222241266681\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industry and Higher Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09504222241266681","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Toward predicting entrepreneurial activity among Moroccan Entrepreneurs: A machine learning approach
Entrepreneurial activity, a subject of enduring intrigue among scholars, continues to captivate attention, especially in distinct contexts such as Morocco. This study undertakes the formidable task of comprehending and forecasting entrepreneurial activity using the comprehensive Global Entrepreneurship Monitor (GEM) dataset for Morocco. Employing a diverse range of machine learning classifiers, including logistic regression, random forest, support vector machines, gradient boosting, and K-nearest neighbors, our research excels in predicting entrepreneurial activity with remarkable accuracy. Notably, support vector machines emerge as the most potent classifier, achieving an impressive accuracy rate of 95.33%. These findings transcend the inherent complexities of understanding the infrequent nature of entrepreneurial activity, providing invaluable insights into predictive modelling within the Moroccan entrepreneurial landscape. This research not only advances our comprehension of entrepreneurship but also paves the way for informed policymaking and the nurturing of a thriving entrepreneurial ecosystem. This underscores the critical importance of effective data handling and model refinement in achieving these milestones.
期刊介绍:
Industry and Higher Education focuses on the multifaceted and complex relationships between higher education institutions and business and industry. It looks in detail at the processes and enactments of academia-business cooperation as well as examining the significance of that cooperation in wider contexts, such as regional development, entrepreneurship and innovation ecosystems. While emphasizing the practical aspects of academia-business cooperation, IHE also locates practice in theoretical and research contexts, questioning received opinion and developing our understanding of what constitutes truly effective cooperation. Selected key topics Knowledge transfer - processes, mechanisms, successes and failures Research commercialization - from conception to product ''Graduate employability'' - definition, needs and methods Education for entrepreneurship - techniques, measurement and impact The role of the university in economic and social development The third mission and the entrepreneurial university Skills needs and the role of higher education Business-education partnerships for social and economic progress University-industry training and consultancy programmes Innovation networks and their role in furthering university-industry engagement