[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120589-en":3,"doc-seo-120589-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120589,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Profiling Social Entrepreneurship Orientation Through Machine Learning Predictive Modeling - Research Article","This study predicts Social Entrepreneurship Orientation (SEO) among youth using machine learning to build a robust predictive model. Based on a large cross-country European youth survey, the strongest determinants are cognitive–attitudinal signals, including environmental goal concern, purpose-driven motivation, and positive perceptions of entrepreneurship. Feature-importance analyses quantify each predictor’s contribution, outperforming demographic or background variables such as gender, education, or nationality. Findings support educators, policymakers, and impact investors seeking to identify, support, and scale mission-driven innovation and human-capital trends.","Corporate Social Responsibility and Environmental Management  \nRESEARCH ARTICLE  OPEN ACCESS   \nProfiling Social Entrepreneurship Orientation Through Machine Learning Predictive Modeling  \nÁngel Peiró-Signes1  | Colin Donaldson2  | Marival Segarra-Oña1   \n1Management Department, Business School, Universitat Politècnica de València, Valencia, Spain | 2EDEM Business School, Valencia, Spain Correspondence: Marival Segarra-Oña ([maseo@omp.upv.es](maseo@omp.upv.es))  \nReceived: 7 July 2025 | Revised: 10 November 2025 | Accepted: 16 November 2025  \nKeywords: entrepreneurship | environmental concern | machine learning | profile | social change | social innovation | youth values  \nABSTRACT  \nThis study examines the prediction of Social Entrepreneurship Orientation (SEO) in youth using machine learning techniques to generate a robust predictive model. Drawing on a large cross-country survey of European youth, results indicate that cognitive–attitudinal indicators—such as concern for environmental goals, purpose-driven motivation, and positive perceptions of entrepreneurship, emerge as the most powerful determinants, providing a stronger predictive signal for SEO than demographic or background variables such as gender, education or nationality. Feature-importance analyses quantify each predictor's contribution. These insights suggest a shift in youth entrepreneurship from economic self-interest toward socially and environmentally conscious innovation. The implications are substantial for educators, policymakers, and impact investors aiming to identify, support, and scale socially oriented entrepreneurial potential. This paper offers both a methodological contribution to forecasting social change and a strategic tool for anticipating human capital trends in mission-driven innovation.  \n1 | Introduction  \nThe current global polycrisis, defined by the convergence of ecological, economic, and institutional disruptions (Henig and Knight 2023), has exposed the limits of conventional problemsolving approaches and emphasized the need for socially driven innovation at scale. Accordingly, Social Entrepreneurship (SE) as a hybrid model of value creation integrating market-based mechanisms with a mission to generate social and environmental impact has consolidated as a field of legitimate inquiry (Bonfanti et al. 2024; Gupta and Srivastava 2024) .  \nIdentifying future-oriented agents of change has become a strategic priority. Young social entrepreneurs may represent a catalytic force for transformation. Several studies reveal that today's youth are driven to create positive social change (e.g., Bosma et al. 2016) and that life encounters with social injustice or hardship have heightened their awareness of such issues (Erickson and Lombardo 2023). That being said, many younger individuals  \nremain disaffected by the formal economy due to a lack of meaningful and quality employment opportunities. Consequently, SE holds significant potential to inspire young people to actively contribute to achieving social goals, such as environmental protection or inclusion and diversity of, and in, employment. The current investigation is driven by the belief that in an era defined by complexity, crisis and wicked problems (Elia and Margherita 2018; Grewatsch et al. 2023; Sharma et al. 2024), the world urgently needs entrepreneurial solutions that are socially grounded and ethically informed; for this, the capacity to locate a youthful generation who are positively inclined and motivated to lead this change is required (Stevens et al. 2015) .  \nA proliferation of efforts has explored the breadth and impact of social enterprises and their founders (Hidalgo et al. 2024) . Most noteworthy work comes from internationally recognized bodies and initiatives including the Global Entrepreneurship Monitor (Bosma et al. 2016), the International Centre of Research and Information on the Public, Social and  \nThis is an open access article under the terms of the Creative Commons","cbCaiickBkPzUD8Y","https://ap.wps.com/l/cbCaiickBkPzUD8Y","pdf",524907,1,15,"English","en",105,"# Introduction\n## Background and rationale\n## Social entrepreneurship as socially grounded innovation\n## Need to identify youth-oriented change agents\n## Prior research and institutional efforts","[{\"question\":\"What does the study aim to predict using machine learning?\",\"answer\":\"The study predicts Social Entrepreneurship Orientation (SEO) in youth by constructing a robust predictive model.\"},{\"question\":\"Which factors most strongly determine SEO in the results?\",\"answer\":\"Cognitive–attitudinal indicators dominate, especially environmental goal concern, purpose-driven motivation, and positive perceptions of entrepreneurship.\"},{\"question\":\"How do the model’s predictors compare with demographic variables?\",\"answer\":\"The predictive signal from attitudinal and cognitive indicators is stronger than signals from demographic or background variables such as gender, education, or nationality.\"}]","Profiling Social Entrepreneurship Orientation Through Machine Learning Predictive Modeling - 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