인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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.
초록·키워드
With the increasing prevalence and sophistication of non-tariff barriers, small and medium-sized enterprises (SMEs) face structural challenges in establishing independent response systems. Guided by the resource-based view (RBV), this study empirically examines the determinants of SMEs’ trade barrier response capability (TBRC) and further interprets the construct through the lens of dynamic capability theory. Using survey data from 209 exporting SMEs in Korea, we conducted t-tests and multiple regression analyses. The results indicate that firms with an export-dedicated unit exhibit significantly higher TBRC compared to those without. Regression analyses show that product development capability (PDC), process improvement capability (PIC), top management capability (TMC), and training-based qualitative capability of the export unit (EDOCE) consistently enhance TBRC, while mere headcount expansion (EDOCN) does not. Notably, PIC and TMC exert the strongest effects, suggesting that effective responses to trade barriers are reinforced more by operational stability and leadership than by product innovation alone. Academically, this study extends RBV into the regulatory compliance context by conceptualizing TBRC as a dynamic capability that involves sensing, seizing, and reconfiguring in response to evolving trade barriers. It also highlights the theoretical value of qualitative resources and organizational learning. Practically, the findings underscore that SMEs should prioritize establishing export units, institutionalizing training, stabilizing processes, and strengthening managerial leadership as core strategies for coping with trade barriers. Limitations include the use of cross-sectional and self-reported data, a restricted sample scope, and constrained operationalization of variables. Future studies should employ panel data, objective indicators, and multidimensional measures to further refine the mechanisms underlying TBRC formation.
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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