국내 산학협력 양상 연구

Abstract

Objectives This study aims to review the aspects of domestic industry-academic cooperation by keyword network analysis of web data from 2018 to 2021, focusing on impacts of each performer-government, university, industry-on the industry-academia cooperation(IAC) system before and after the COVID-19 phenomenon.
 Methods Keyword network, keyword centrality, ego network, and topic modelling analysis were conducted to examine 1) each influence from the government, university, industry on IAC system, 2) changes its influence after the COVID-19 phenomenon, and 3) the degree to IAC policies were reflected in the aspects of IAC, analysing 5,000 documents collected from web news and blogs.
 Results As a result of keyword network analysis, university, industry, and support project were found to be the top frequent keywords and also at the center of keywords, showing the characteristics of IAC that industry and universities focus on jointly designing and operating government-supported IAC programs. Analysis of ego networks shows that since COVID-19, industry and government networks have grown in size, while recruitment and field practice networks have decreased in size. Keywords such as job creation, SME support, and new industrial talent training, which are major government industry-academic cooperation policies during the text collection period, were partially derived as major areas in topic modeling analysis, but showed different patterns before and after COVID-19.
 Conclusions Government, university, industry were found to be the key players in determining the aspects of the IAC system, and the most influential subject was university over the past four years, actively cooperating with companies in the educational field through government-led development projects. The government's IAC policy for job creation was also reflected in the IAC aspect, but it seems to be limited by the COVID-19 factor. Implications for the role of government, university, and industry in sustaining IAC are discussed.