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
This study analyzed research trends based on 158 academic journal papers (1998–2026.06) on domestic tacit knowledge research, selected through a PRISMA-based systematic review of the RISS database. The purpose is to identify the overall landscape of domestic tacit knowledge research, summarize patterns of theoretical framework utilization, and propose future research directions through content analysis. Results show that research is most heavily concentrated in fields outside business and education—such as martial arts and physical education—followed by pedagogy and business administration, with AI-related research increasing markedly since 2020. Nonaka's SECI model remains the most widely utilized theoretical framework, but its dominance is declining, while Embodied Cognition theory and reinterpretations of Polanyi's original texts are steadily gaining ground. Findings are classified into six categories: conceptual/theoretical frameworks, transfer/sharing conditions and barriers, acquisition and internalization processes, organizational performance linkage, AI/digital integration, and practical tools/systems. Three historical phases are identified: introduction (~2004), multi-disciplinary expansion (2005–2019), and AI/digital integration (2020–). This study contributes theoretically by providing the first empirical evidence of a paradigm shift in domestic tacit knowledge research—from SECI-model dependency toward embodied-cognition and original-Polanyi-based reinterpretation—thereby offering an evidence-based diagnostic map for the field's theoretical diversification, and proposes future directions including mixed-methods designs and Korean contextualization.
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.
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