This study examined the role of Higher Educational Institutions (HEIs) in promoting, creating, and sustaining social innovation. Recently, HEIs have extended their contribution beyond the traditional function of teaching and research to perform in socio-economic problem-solving. Considering the increasing trends of higher education involvement in social innovation practices, this study tries to examine the tools such as learning processes and systemic thinking approach that could be helpful to align the function and responsibilities of HEIs towards social innovation. The objective is to develop a theoretical understanding of the “co-creation for social innovation” concept and to understand the functions and activities of HEIs that can contribute to this process. To promote co-creation for social innovation, HEIs should actively encourage collaborative learning tools that focus on open platforms for collective action and systemic change that help them to engage with society and strengthen their collaboration with social actors. Different activities such as mutual learning and knowledge diffusion using a transdisciplinary approach, technology-based learning and collaboration, and relational transformation are key enablers that can promote social innovation.

This study examined how green technology (GT) has evolved recently in the domain of national research and development (R&D) in Korea. The analysis considered social network analysis by using keywords of national R&D projects in environmentally benign (‘green’ in this paper) technologies. Data for analysis were collected from the R&D database of the National Science and Technology Information Service (NTIS). By selecting the official GT category in the NTIS’s database, we gathered 53,896 GT R&D projects from 2011 to 2016 inclusive. By constructing a co-occurrence matrix with keywords, we conducted a centrality analysis (including degree, closeness, betweenness centrality) and visualized the network matrix to display a cluster map. This study presents the following findings: (1) the chronical trends of GT R&D, (2) focused fields of GT R&D, (3) the relations between keywords and GTs, and (4) the characteristics and problems in GT R&D. The paper ends with suggested policy implications to boost national GT R&D and to develop GTs in the future.

This paper reviews the development of South Korea’s national research and development (R&D) in graphene technology, focusing on projects that have been classified as “green” technology. A total of 826 projects (USD 210 billion) from 2010 to 2019 were collected from the National Science and Technology Information Service (NTIS), which is full-cycle national R&D project management system in South Korea. Then we analyzed its R&D trend by conducting diverse text mining methods including frequency analysis, association rule mining, and topic modeling. The analysis suggests that the number of graphene green technology (GT) R&D projects and the research expenses will show a rising curve again in the incumbent government along with the implementation of the Korean New Deal policy, which integrates the Green New Deal and the Digital New Deal.

Effectiveness of Government R&D on Firm's R&D Spending Government R&D Investment;Firm R&D Investment;Firm Size;Two-way Error Component Model; This study empirically analyze the effect of government R&D investment to find out whether it complements or substitutes for the firm R&D. In order to do it panel data set was constructed for the period of three years from 2012 to 2014 based on the number of 1301 data by utilizing national technology information service(NTIS) and publicly announced financial statement. Analysis was implemented in consideration of size of the firm(large corporation, small and medium sized firm) of which sample was obtained from only listed company. The result of two-way fixed effect model and two-way random effect model is as follows. In case of large corporation, government R&D investment has an effect of substitute for the company's R&D on the other hand, small and medium sized firm shows an complementary effect. It verifies that current R&D policy is appropriate. Therefore government's direct subsidy is expected to be successful to fertilize firm's innovation by allocating government R&D budget efficiently.

Although graphene has created a strong wave of enthusiasm after its discovery in 2004 and the subsequent Nobel prize awarded to the two detectors in 2010, there is no clear information how people with special interests in it feel toward the possibility of its commercial value over time. The objective of this study is to identify graphene sentiment shift and major discussion topics among the interested public on Reddit by presenting an effective tool for extracting public opinions in social media. To achieve this, we processed 11,287 comments collected from the Reddit from 2010 to 2021, and applied text mining techniques, including sentiment analysis. This study found that over half of the comments were positive toward graphene, and specific sentiment fluctuated commensurate with major events, for example, Nobel Prize, commercialisation delay and wide applications to the industry. Our research proved that public sentiments toward graphene technology follow a similar trend to the hype cycle by deriving a sentiment hype cycle for graphene. This study provides a convincing case for understanding shifts in the sentiments of the interested public regarding new technology, such as graphene, which will interest graphene technology R&D experts and policy-makers.

This study is aimed at seeking policy implications for the policy makers of South Korean government and finding a direction to support R&D institutions in performing R&D activities more efficiently, by analyzing the factors influencing technology transfer of the national R&D projects. The data retrieved from NTIS (National Science & Technology Information Service) was used in analyzing the results of 575 projects with 1,903 cases of technology transfer, performed by the Ministry of Science, ICT and Future Planning, between 2002 and 2012. We found that there were significant differences between the government funded institutions and the universities and between basic R&D and applied ones. We also discovered that the government funded institutions did not necessarily take a better position than the universities in terms of the quantity of technology transfer. Lastly, the applied R&D of the universities was very vulnerable in terms of technology transfer.