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.
초록·키워드
Abstract This article presents a novel model for understanding the structure and dynamics of business networks, emphasizing the role of propensities to connect and cooperate as key drivers. The model incorporates behavioral elements and imperfect information updates, departing from traditional rational actor approaches. Starting from the theoretical background, several propositions are outlined, such as dynamism, connection choices, costs, strategy selection, information update, and the update based on experiences. Through simulations, the study successfully demonstrates that the proposed model effectively captures essential characteristics of business networks, including reciprocity, complexity, adaptation, and cooperation. The findings highlight the significance of propensities to connect and cooperate in shaping network structure, evolution, and stability. Particularly, higher propensities to cooperate and connect lead to denser and more cohesive networks, fostering reciprocity, stability, and network performance. The increase only in connection propensities does not have the same result. The lower cooperation propensities result in scale-free networks and asymmetrical distribution of cumulative payoffs. This highlights a crucial insight: different levels of cooperation lead to distinct network properties. Practical implications, increasingly relevant with the rise of digital platforms and metaverse, suggest targeted interventions to enhance network effectiveness, such as incentivizing cooperation, reducing relationship costs, and promoting a culture of trust and collaboration. While providing valuable insights, certain limitations exist, such as not considering the influence of preexisting economic or social structures, equalizing costs and payoffs among actors, and overlooking specific reasons for network connections. Future research should address these refinements and explore their effects on network structure and process characteristics.
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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