산업융합의 거시적 양상을 다루는 기존 연구는 주로 지식과 기술이 구체화된 특허나 재화/서비스의 산업간 네트워크에 초점을 두었다. 이 논문은 지식과 기술을 체화한 인적자원이 산업과 직업의 융합을 주도하는 또 하나의 중요한 매개체라고 본다. 최근 5개년도 GOMS 자료에 나타난 대졸자 전공 및 일자리 정보를 활용하여 산업(직업)융합의 거시적 양상과 그 구조를 이해하기 위한 분석전략을 제시한다. 구체적으로 전공과 일자리 정보를 토대로 전공↔산업(직업)의 비대칭 이분네트워크를 구축하고, 산업(직업)의 적소범위, 관련 전공의 평균 적소범위라는 두 축을 교차하여 전문주의, 종합주의, 국지적 융합, 융합 등 총 4가지 산업(직업)군을 분류하였다. 융합산업(직업)은 특정 분야에서 국지적 융합성이 높은 다수의 전공으로부터 인적자원을 폭넓게 공급받는 산업(직업)이라고 볼 수 있다. 탐색적 분석결과를 토대로 함의를 도출하고 향후 과제를 제시한다.
산업융합의 거시적 양상을 다루는 기존 연구는 주로 지식과 기술이 구체화된 특허나 재화/서비스의 산업간 네트워크에 초점을 두었다. 이 논문은 지식과 기술을 체화한 인적자원이 산업과 직업의 융합을 주도하는 또 하나의 중요한 매개체라고 본다. 최근 5개년도 GOMS 자료에 나타난 대졸자 전공 및 일자리 정보를 활용하여 산업(직업)융합의 거시적 양상과 그 구조를 이해하기 위한 분석전략을 제시한다. 구체적으로 전공과 일자리 정보를 토대로 전공↔산업(직업)의 비대칭 이분네트워크를 구축하고, 산업(직업)의 적소범위, 관련 전공의 평균 적소범위라는 두 축을 교차하여 전문주의, 종합주의, 국지적 융합, 융합 등 총 4가지 산업(직업)군을 분류하였다. 융합산업(직업)은 특정 분야에서 국지적 융합성이 높은 다수의 전공으로부터 인적자원을 폭넓게 공급받는 산업(직업)이라고 볼 수 있다. 탐색적 분석결과를 토대로 함의를 도출하고 향후 과제를 제시한다.
Prior research regarding the macro patterns of industry convergence has focused on the inter-industry patent network and cross-industry movements of products or services. This article provides a novel approach, according to which human resources embodying explicit and implicit knowledge and technolo...
Prior research regarding the macro patterns of industry convergence has focused on the inter-industry patent network and cross-industry movements of products or services. This article provides a novel approach, according to which human resources embodying explicit and implicit knowledge and technologies are important media driving industry convergence. Drawing on GOMS data (2015-2019) and using information of university graduates' academic majors and their occupations, this article proposes an analytic strategy by which to understand the macro patterns and structural features of industry convergence. Specifically, we build unbalanced bipartite networks of major-industry (occupation) relations, and construct the measures of the industry's niche width and the measure of the average degree of convergence of majors that each industry is linked to. By crossing the two measures, we identify four groups of industries(occupations); specialist, generalist, partial convergence, and full convergence. The convergence group is composed of industries (occupations) that acquire human resources from a number of academic majors each of which plays a role of glue connecting several local industries.
Prior research regarding the macro patterns of industry convergence has focused on the inter-industry patent network and cross-industry movements of products or services. This article provides a novel approach, according to which human resources embodying explicit and implicit knowledge and technologies are important media driving industry convergence. Drawing on GOMS data (2015-2019) and using information of university graduates' academic majors and their occupations, this article proposes an analytic strategy by which to understand the macro patterns and structural features of industry convergence. Specifically, we build unbalanced bipartite networks of major-industry (occupation) relations, and construct the measures of the industry's niche width and the measure of the average degree of convergence of majors that each industry is linked to. By crossing the two measures, we identify four groups of industries(occupations); specialist, generalist, partial convergence, and full convergence. The convergence group is composed of industries (occupations) that acquire human resources from a number of academic majors each of which plays a role of glue connecting several local industries.
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