• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.222-223
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• 1997

By analyzing the successful innovation case of potato microtuber mass production technology, a representative case of technology-push type creative innovation in an imitation oriented research culture, this paper attempts to figure out conceptual model of creative innovation that is initiated by the public laboratories in catching-up country, Stages of creative innovation can be divided into the internal R&D stage and the external commercialization stage. Success of the internal R&D stage depended on autonomy to secure creative research idea and commitment of individual researchers. Psychological pressure evoked from sportlights of mass media and commitment of sponsor increased the intensity of research efforts of the researcher Recognition of research problem and its significance was intensified by site visits of agricultural fields, and the recognized higher impacts of expected research results and knowledge creation achieved were a fundamental source of self-motivation. In the stage of commercialization stage, various legal, socio-economic, and psychological barriers were confronted. In a catching-up country lacking of experiences of creative innovation, creative innovation process can be regarded as a barrier elimination and cultural revolution process. Among the barriers, psychological refusal of farmers to corn-sized potato seeds was critical, which finally enforced to further researches to enlarge the size of potato seeds. In addition, the researcher has concentrated his research efforts in one specialized research area by getting a series of similar research project funds rather than diversification. It was lucky for him to have a chance to carry out a series of similar researches in one research area during the last 10 years. In getting research funds from government and private companies continuously in one research area, both internal and external promoters played significant roles.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.