• Journal of Korea Technology Innovation Society
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• v.14 no.3
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• pp.647-663
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• 2011

Company's interest in the work place training and investment has been growing continuously because the talent of human resource is the competitiveness itself in the knowledge based society. However, corporate training programs mainly have focused on large companies and SMEs despite the economic business volume have been treated too lightly so far. This paper regards corporate training programs with one of the methods for SMEs innovation and proposes the smart learning in the smart device diffusion. Concretely, this paper analyzes the utilization intention, each attribute and level in smart learning characteristics using conjoint analysis. The result shows that SMEs have positive response for smart learning acceptance and SMEs consider significantly the usage fee type and location with the difference between regular employee and administrator. Specially, interactive communication and customized contents are preferred in the training type. Smart learning can be used as strategic means in supporting the value innovation and enhancing the absorptive capacity in SMEs innovation process.

• Journal of Korea Technology Innovation Society
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• v.17 no.2
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• pp.380-420
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• 2014

We increasingly see the importance of employees acquiring enough expert capability or innovation capability to prepare for ever growing uncertainties in their operation domains. However, despite the above circumstances, there have not been an enough number of researches on how operational input components for employees' innovation outcome, innovation activities such as acquisition, exercise and promotion effort of employee's innovation capability, and their resulting innovation outcome interact with each other. This trend is believed to have been resulted because most of the current researches on innovation focus on the units of country, industry and corporate entity levels but not on an individual corporation's innovation input components, innovation outcome and innovation activities themselves. Therefore, this study intends to avoid the currently prevalent study frames and views on innovation and focus more on the strategic policies required for the enhancement of an organization's innovation capabilities by quantitatively analyzing employees' innovation outcomes and their most suggested relevant innovation activities. The research model that this study deploys offers both linear and structural model on the trio of learning, innovation capability and innovation outcome, and then suggests the 4 relevant hypotheses which are quantitatively tested and analyzed as follows: Hypothesis 1] The different levels of innovation capability produce different innovation outcomes (accepted, p-value = 0.000<0.05). Hypothesis 2] The different amounts of learning time produce different innovation capabilities (rejected, p-value = 0.199, 0.220>0.05). Hypothesis 3] The different amounts of learning time produce different innovation outcomes. (accepted, p-value = 0.000<0.05). Hypothesis 4] the innovation capability acts as a significant parameter in the relationship of the amount of learning time and innovation outcome (structural modeling test). This structural model after the t-tests on Hypotheses 1 through 4 proves that irregular on-the-job training and e-learning directly affects the learning time factor while job experience level, employment period and capability level measurement also directly impacts on the innovation capability factor. Also this hypothesis gets further supported by the fact that the patent time absolutely and directly affects the innovation capability factor rather than the learning time factor. Through the 4 hypotheses, this study proposes as measures to maximize an organization's innovation outcome. firstly, frequent irregular on-the-job training that is based on an e-learning system, secondly, efficient innovation management of employment period, job skill levels, etc through active sponsorship and energization community of practice (CoP) as a form of irregular learning, and thirdly a model of Yί=f(e, i, s, t, w)+${\varepsilon}$ as an innovation outcome function that is soundly based on a smart system of capability level measurement. The innovation outcome function is what this study considers the most appropriate and important reference model.

• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.219-241
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• 2014

In general, Innovation Systems consists of actors, institution and network. And mutual learning through the innovation capacity is to be increased, which ultimately evolved through innovation that explains the system itself. In this study, Senior Friendly Industry in Korea for 'Innovation systems formation and evolution of the process' a dynamic perspective. The purpose of this paper is to provide suggestions that innovation policy implications for technological innovation in this sector or similar industry like a health industry, environment industry and etc. Study, a new type of formation and the evolution of the innovative system was confirmed. As the Senior Friendly Industry is a new case for innovation studies, our study may provide new research opportunities to the academia.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.05b
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• pp.63-70
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• 2002

This paper is to illuminate the dynamic relationship between the technological learning mechanism and international political intervention. Besides conventional techno-economic factors, international political factors are emphasized as external factors in the technological learning mechanism. Being influenced by international political intervention, the evolutionary path of technological capabilities is not incrementally cumulated and organizational process is not autonomously performed, either. In order to mitigate the impact of the international political intervention, DCs make efforts to develop technological capabilities step-by-step in line with current and future civilian industrial demand.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.179-185
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• 2022

The purpose of this study is to provide new implications for the development and operation of programs and comparative studies in university policy by examining how core competencies of university students participating in tutoring programs affect learning satisfaction. For this purpose, 33 university students participated in the tutoring program before COVID-19 and 72 university students participated in the tutoring program after COVID-19 were surveyed and statistically processed. As a result, first, there was a positive correlation between self-innovation, challenge, communication, harmony, sincerity, problem solving and learning satisfaction among the components of core competencies of H university before COVID-19. There was a positive correlation between self-innovation, challenge, communication, harmony, sincerity, problem solving and learning satisfaction of core competencies after COVID-19. Second, in the pre-COVID-19 period, core competence had a high explanatory power on learning satisfaction, but there was no statistically significant factor in each of the remaining components except for the challenge. After COVID-19, core competence was secured with a high rate of explanatory power, self-innovation and harmony among the components of core competence were found to have a positive effect on learning satisfaction, and challenge was found to have a negative effect on learning satisfaction. The implications of this study are that when students participate in the tutoring program, it is necessary to emphasize the challenge factors and when they proceed in non-face-to-face, it is necessary to emphasize the self-innovation factors or the harmony factors rather than the challenge factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.274-281
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• 2015

Firms are becoming increasingly dependent on external resources for a competitive advantage in environmental turbulence, the requisite speed of organizational change. Therefore, they focused on an 'Open Innovation'. This study examined the relationships among entrepreneurial orientation, learning orientation, open innovation, and new product development. The results provide broad support for the following: (1) entrepreneurial orientation and learning orientation strongly drive open innovation, (2) open innovation has a significant effect on new product development, and (3) learning orientation has a positive effect on open innovation and new product development. These results provide unique insight into how firms develop new product through open innovation. This study also addresses the implications of these results and the possible limitations of the research.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.707-719
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• 2023

In the Fourth Industrial Revolution era, AI technologies, such as Chat GPT, have moved beyond assisting to actively analyzing data and providing solutions. This research assessed Chat GPT's e-service quality's influence on perceived value, innovativeness, and subsequent learning outcomes. Findings revealed that while ease of use and responsiveness weren't significant, safety and reliability were positively related to perceived value and innovativeness. A negative correlation was found between trustworthiness and perceived value. Users who saw Chat GPT as valuable and innovative experienced enhanced learning. The study emphasizes the need for guidelines in deploying Chat GPT academically. Given Chat GPT's recent introduction, further nuanced research is necessary.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.05b
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• pp.221-228
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• 2002

In viewing R&D as a series of activities of problem solving in the course of technological learning in developing countries, national R&D is very likely to play an pivotal role in incubating and improving industrial technology and competitiveness, especially for S&T-intensive industries, such as nuclear fuel industry. To this end, the national R&D needs to be primarily designed to reinforce technological capabilities of the country including government research institutes (GRIs), industrial firms, etc., from the initial stage of absorption as well as at the innovation stage. Based on this strategic role, national R&D performed by GRIs should provide scientific knowledge and core technologies which are required for the absorbption of foreign technology and development of indigenous technological capabilities with which domestic technological efforts are made to successfully achieve not only technological imitation but also innovation in global sense.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.192-201
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• 2020

This study commercialization of 5G will enable transformation of university education. This study identifies five attributes (device type, learning place, learning content, learning field and expense payment) and corresponding levels to study the impact of 5G in the future of university education. The attributes and the levels are then combined into few 5G education service alternatives for respondents to rank. 102 students ranked the alternatives based on their preferences and intent to use. Results indicate that the intent to use 5G-based education service was high with 86% and the most important factor was expense payment (37%), followed by learning field (26%), learning content (24%), device type (8%) and learning place (5%). Specifically, students preferred smart device, practical and experiential content, ubiquitous (no limitation of space and time) learning, practical education and free rate when adopting 5G-based education service. These will provide implications to accelerate adoption of and exploitation of 5G for innovating university education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.