인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Faculty startups have emerged as a driving force for growth in an era of low economic expansion, creating high value-added outcomes based on original technologies. This study explores two key research questions. First, can machine learning approaches be appropriately applied to predict the performance of faculty entrepreneurship? Second, do different types of faculty entrepreneurship performance—quantitative and qualitative—require different machine learning models? To address these questions, the study employed Linear Regression (a linear model), Support Vector Regression (SVR) (a kernel-based model), and tree-based ensemble models (Random Forest, GBM, and XGBoost). The results indicate that while the most suitable model varied by performance type, the key predictors influencing startup performance were limited in number and showed consistent patterns. By conducting quantitative and qualitative analyses of faculty startup performance through machine learning that captures nonlinear relationships, this study identifies core predictors and policy implications for each performance type. Building on a resource-based view, it proposes a data-driven framework for predicting faculty startup outcomes and provides empirical evidence to support the strengthening of university innovation ecosystems. Practically and policy-wise, it offers guidance for strategic planning and support measures aimed at enhancing the sustainable growth and performance of faculty startups as key engines of university innovation.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-095549263
