• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.174-194
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• 2014

Collaborative technology development is now one of the most significant modes of activity in the global scientific community. However, the international cooperation of science and technology simultaneously provides opportunities and challenges, and the results of global R&D collaboration can be positive or negative as the cooperation conditions of the parties may be different according to the types or characteristics of the participants and the pattern, purpose, and motivation of cooperation. In order to minimize the risk and improve the performance of cooperation, more comprehensive as well as micro-level research is needed. This study investigates a case of successful collaborative R&D conducted by several firms, universities, and public research organizations in both Korea and the U.S.A. The aim of this study is to identify the factors of successful R&D collaboration.

• Journal of Distribution Science
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• v.20 no.12
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• pp.99-108
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• 2022

Purpose: This study attempted to identify determinants affecting research collaboration and R&D distribution activities, especially regarding facility and equipment leasing of small and medium enterprises (SMEs) in South Korea. The objective of this study was to find the most significant firm characteristics that affect firms participating in an R&D collaboration and distribution program and investing in R&D in terms of leasing payment for equipment. Research design, data, and methodology: This study analyzes which SMEs' characteristics influence external research cooperation activities by examining the SMEs that received government support for equipment leasing using multiple regression analysis and residual plots. The survey combined two databases: 1) a fact-finding survey of participating firms by the Ministry of SMEs and Startups, and 2) leasing information by the Korea Association of University, Research Institute and Industry. Results: The study found that firm size positively impacts R&D investment, R&D collaboration and distribution. Conclusions: The study provided evidence to policymakers and government officials that firms with more employees will more likely participate in government support programs. The study results also prove that government officials believe firm location does not impact R&D investment, R&D collaboration and distribution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.22-31
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• 2023

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.1-12
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• 2019

Collaborative research and development (R&D) has been encouraged based on the belief that knowledge spill-over is mutually beneficial for partners. Although the benefits are supported by science and technology policy research, the risk of R&D collaboration has not been extensively discussed. Two independent studies suggest that there are risks associated with the overuse of collaborative research frameworks. Two sets of R&D collaboration data were analyzed: between the national bio-technology research program and 1) Data Envelop Analysis (DEA), and 2) between Stochastic Frontier Analysis (SFA). In the case of SFA, output measures were integrated into a single output, with weights extracted from research programme managers' responses to the questionnaire. While the DEA result demonstrated the inefficiency of collaborative research, SFA did not. Unlike previous research highlighting risks associated with disclosing proprietary R&D and potential conflict of interest, our study indicates that the transaction's social cost affects collaborative research efficiency. Therefore, governments promoting R&D collaborations should be carefully managed, and policy makers must reconsider the strict conditions governing compulsory collaborative R&D programs.

• STI Policy Review
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• v.7 no.1
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• pp.17-39
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• 2016

Despite the importance of university-industry collaboration, issues pertaining to the characteristics of collaborating firms, their modes of interaction, and the relationship between these modes and outcomes are not well-researched. The impact of country's development on these issues is also unclear. This case study examines Japan and Thailand-respectively representing developed and developing countries-and features the following key findings: 1) the characteristics of firms affect modes, with large Japanese firms being more collaborative with universities, whereas Thai SMEs significantly collaborate more with universities; 2) the relationship between modes in Thai firms is stronger than those of Japanese firms because in Thailand, perhaps due to weak technological capacity, R&D collaboration is conducted alongside university consultancy services; and 3) in Japan, R&D and human resource development collaboration lead to product innovation, whereas different outcomes are expected from different modes in Thailand. Apparently, trivial informal collaborations do have significant impact on innovation.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.122-158
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• 1998

This study empirically examined different patterns of collaborative R&D project with their key success factors(KSFs), using data from 82 projects in the Korean electronic parts industry. The patterns of R&D collaboration were categorized into 4 types by two criteria development motive(technology Push/market pull) and Project initiator (focal firm/partner). The bivariate relationships revealed that project characteristics (technological complexity, market uncertainty), management characteristics (participation in project formulation), problem solving characteristics(problem solving performance of the focal firm, users active role in problem solving, active role of university or research institute in problem solving) and success rates appear to be different among four types of collaboration. Each type of collaborative R&D projects also had different KSFs. The KSFs of type 1 (technology Push and focal firm initiation), for instance, include the strategic importance of the project, focal firms share of cost, active role of university or research institute in problem solving, while those of type 4(market pull and customer initiation) cover reliability of partner relationship, a time at partners involvement, information sharing. The findings suggest that the different contingencies brought different patterns and KSFs of collaborative R&D project, since different information, resources, and partners roles were needed to successfully implement the projects according to development motive and project initiator Finally, managerial, policy, and theoretical implications for the collaborative R&D activities in the Korean electronics parts industry were discussed, based on empirical results of this study.

• Knowledge Management Research
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• v.14 no.5
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• pp.15-34
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• 2013

In a turbulent environment, it is important for a firm to obtain competitiveness through acquiring new technologies and new knowledge. Many studies has shown that R&D has positive effects on innovation and performance. However, the limitation of resources and research personnel for SMEs leads to the needs of collaboration between government R&D and SMEs. The quantitative results of government R&D comes in the form of patents or publication of papers while the qualitative results comes in the form of commercialization (revenue increase or cost reduction) of the SMEs acquiring the technology through technology transfer. Korean government invested 16.9 Trillion Won in 2013 and achieved remarkable quantitative results but the qualitative results are still yet to be improved. The technology transfer rate in 2011 was 26.0% while the success rate of commercialization of the transferred technology is only 23.6%. Therefore it is meaningful to study the success factors of the technology commercialization in Korea. In this study, a case of technology commercialization between ETRI and Initech is studied. Initech was a venture firm which started in 1997 with 2 employees and after transfer of technology from ETRI in 1999, it has grown to a firm of 238 employees. In this study, Jolly's commercialization model was used to analyze the success factors of the commercialization. It was shown that four main factors that leads to the success are (1) mobilizing interest and endorsement through customer oriented R&D by ETRI, (2) mobilizing resources for demonstration through systematic technology commercialization process by ETRI, (3) mobilizing market constitutents through government standardization that leads to the reliability of the participants, and (4) mobilizing complementary assets for delivery by management strategies of Initech.

• Journal of Korean Library and Information Science Society
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• v.44 no.4
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• pp.209-231
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• 2013

This study is a comparative analysis of R&D performance and research collaboration relationship among ten ocean research institutes in the Republic of Korea and foreign nations through analyzing SCI publications from 2008 to 2012. The result indicated that SIO, WHOI, and NOC had the highest number of publications, while research institutes in East Asian nations including NFRDI, KIOST, IOCAS, and JAMSTEC had the notable growth in publication. In terms of number of publication per researcher, SIO, WHOI, and NOC had relatively high number. In terms of publication per research budget (1 billion KRW), number of publication was high in the order of IOCAS, NOC, GEOMAR, SIO. The number of Citations Per Publications (CPP) was high among WHOI, SIO, NOC, GEOMAR, JAMSTEC, and IFREMER, which are North American, European and Japanese institutes. The average Impact Factor (IF) of journal submission per publication for IFREMER, KIOST and JAMSTEC was relatively increasing, while the number was fluctuating in other institutes. The analysis of research collaboration among institutes around the globe showed that the collaboration in Asia was relatively closed, whereas it was more open in Europe. In the case of Europe and Asia, higher number of research collaboration among nations also increased the quality of submitted articles.

• IE interfaces
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• v.25 no.1
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• pp.87-95
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• 2012

Very special and tentative considerations including emotional aspects are required to apply any new mechanism and methodologies for manufacturing fields due to several reasons. This study reviews the characteristics of manufacturing collaborations through specific cases applied digital engineering to enhance the collaboration performance in manufacturing domains. Two cases of collaboration related with automotive manufacturing process are analyzed to extract meaningful insights for better collaboration model suggestions. The first case deals the robot simulation to find out advance errors in jig and fixture design during the various welding process of body-in-whites. The effective communication protocol to share their idea and agreed schedules are essential for this collaboration. More severe requirement of collaboration between R&D and manufacturing departments are studied in the second case for e-coating process. The invisible barriers among different departments are lowered by the process application of Computer Aided Engineering which can make sure their own interesting effectively. Those technical and managerial suggestions can be used when the information system and standard process are sought to implement and update not only when innovation projects are executed.