• The Journal of Small Business Innovation
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• v.19 no.2
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• pp.53-68
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• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.4
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• pp.89-114
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• 2009

BizCool is an entry-level business education program implemented with a purpose of enhancing the entrepreneurial disposition and capability of the youth through enterprise commencement education of vocational high school students and developing them into economic pillars of future to alleviate the labor shortage crisis of small and medium enterprises and revitalize enterprise commencement. In this study, the factors were deduced and a path analysis was conducted with the purpose of proposing a revitalization plan through an effectiveness analysis of enterprise commencement education for the youth. The result of the analysis revealed that BizCool has effects on the knowledge and understanding of enterprise commencement, and enterprise commencement knowledge also has effects on the understanding of enterprise commencement. In addition, it was found that the enterprise commencement knowledge and the understanding on enterprise commencement have effects on the intention of enterprise commence. In the result of verifying the hypothesis that the understanding of enterprise commencement according to the psychological characteristics of individuals will have a positive effect on the enterprise commencement intention, it was revealed that the risk resignation disposition and patience for ambiguity are having effects but accomplishment desire is not playing a moderating variable role. Through the above path analysis, a conclusion was obtained that the enterprise commencement education has effects on the outcome of enterprise commencement education through the understanding on enterprise commencement. Additionally, the nation needs to actively lead systematic enterprise commencement education within the policy of job creation upon realizing the need for enterprise commencement education of vocational high school, and a revitalization plan was proposed on the five kinds of a conversion of social perception on vocational high school, establishment of institutes specializing in BizCool and cultivation of BizCool specialists, development of various materials and expansion of BizCool program, field-centered lessons, revitalization of enterprise commencement club and personnel and resource support by companies.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Korean Chemical Engineering Research
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• v.57 no.3
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• pp.331-337
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• 2019

Titania has become the most applicable material for photocatalytic application. Nevertheless, titania has the weak point in its wide band gap energy that is mainly activated by UV irradiation. There have been vast research challenges in order to make the wide band gap energy of titania narrow that could be activated in the presence of visible light. Various modifications of titania surface were popular because titania needs to change its surface to respond in visible light. Among the methodological approaches, N-doping to titania can be the alternative candidate because it is facile process and eco-friendly. The activated electron from valence band in N-doped $TiO_2$ migrates to conduction band in the presence of visible light irradiation, which shows photocatalytic activity as well. In this study, focused on the evaluation of nitrogen state after N-doping through brief review. Arguments are still existed in nitrogen states and their different effects on photocatalytic activity. In particular, two nitrogen states are generally reported; substitutional and interstitial states. The research articles regarding N-doped $TiO_2$ are continuously appearing because the potential application of water split in visible light is still fascinate. The future of N-doped $TiO_2$ is also presented by referrals based on various literature.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.87-96
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• 2022

The Ministry of Land, Infrastructure and Transport has also been promoting the commercialization of land transport technology to commercialize the technologies owned by small and medium-sized venture companies, and to support the transfer and commercialization of public technologies. At this point, in order to improve the investment effect of subsequent new projects and to select excellent research institutes, it is necessary to establish a valid evaluation index system suitable for the purpose of the project. The evaluation index system for subsequent new projects should be linked to the project objectives and goals of the preceding project, and should be selected in consideration of existing evaluation indicators to prevent interruption of research results. Therefore, this thesis sets the evaluation index system into multiple scenarios through hierarchical cluster analysis using the evaluation result data for each evaluation committee for small and medium venture companies participating in the land transportation technology commercialization support project, and then analyzes the structural equation model. As a result of scenario analysis, considering the measurement effect of each path representing the causal relationship between evaluation indicators and the effect of each evaluation index on evaluation items, the scenario with the highest impact on the evaluation result was selected as an improvement plan.

• Asian Journal of Turfgrass Science
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• v.20 no.1
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• pp.33-40
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• 2006

Many soil amendments have been used nowadays to improve physical and chmical condition of turf soil, which might ultimately optimize turfgrass growth in golf courses. This study was carried out to Investigate the effects of new organic soil amendment containing pig excreta 50% and sawdust 50% on growth of zoysiagrass (Zoysia japonica L.) and kentucky bluegrass (Poa pratensis L.) in greenhouse. Three applicable treatments with soil mixtures of 10, 20, and 30% (v/v) animal organic soil amendment (AOSA) with sand, were tested for chemical property, physical property, visual quality and root length of zoysiagrass and Kentucky bluegrass. As results, application of $10{\sim}30%$ AOSA mixtures were proper to grow turfgrass in soil nutrition. Especially, the treatment with 20% AOSA mixtures showed 0.7% in organic matter, which meets to green standard of USGA. Also, 30% AOSA mixtures was 1.1% in organic matter, which might be desirable for zoysiagrass-planted golf courses in Korea. It was turned out that addition of AOSA decreased the hydraulic conductivity in soil physical property Because the sand possess high hydraulic conductivity, it is recommended to combine $10{\sim}30%$ AOSA with sand in order to sustain soil balance. The treatment with $10{\sim}30%$ AOSA noticeably increased visual quality of both zoysiagras and Kentucky bluegrass during 90 days. However, treatments with either 20% or 30% AOSA were effective to develop root length of zoysiagrass but treatments with 20% AOSA were more effective than that of 30% AOSA mixtures to promote root length of Kentucky bluegrass at 60 days. In conclusion, considering all vital factors such as visible quality, root growth, organic matter content, and economical efficiency, was taken, it is recommended that a $20{\sim}30%$ mixture of AOSA with sand is good for the growth of zoysiagrass and 20% mixture for Kentucky bluegrass.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.183-228
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• 2024

This study aimed to develop a competency evaluation scale tailored to the specific needs of key talent in the ICT startup sector. Existing competency assessment tools are mostly designed for environments in large corporations or traditional small and medium-sized enterprises, failing to adequately reflect the dynamic requirements of rapidly evolving startups. For startups, where a small number of individuals directly impact company success, key talent is a critical asset. Accordingly, this study sought to create a scale that measures the competencies suited to the challenges and opportunities faced by startups, helping domestic startups establish more effective talent management strategies. The research initially selected 71 items through a literature review and in-depth interviews. Based on expert feedback that emphasized the need for more precise and clear descriptions, the item descriptions were revised, and a total of 65 items were developed through four rounds of content validation. Following preliminary and main surveys, a final set of 58 items was developed. The main survey conducted further factor analysis based on the three broad competency factors?knowledge, skills, and attitude?identified in the preliminary survey. As a result, 10 latent factors emerged: 6 items for task comprehension, 6 items for practical experience (tacit knowledge), 6 items for collaboration, 9 items for management and problem-solving, 9 items for practical skills, 4 items for self-direction, 5 items for goal orientation, 5 items for adaptability, 5 items for relationship orientation, and 3 items for organizational loyalty. The developed scale comprehensively covers the multifaceted nature of competencies, allowing for a thorough evaluation of essential skills such as technical ability, teamwork, innovation, and leadership, which are critical for startups. Therefore, the scale provides a tool that helps startup managers objectively and accurately assess candidates' competencies. It also supports the growth of employees within startups, maximizing the overall organizational performance. By utilizing this tool, startups can build a strong internal talent pool and continuously enhance employees' competencies, thereby strengthening organizational competitiveness. In conclusion, the competency evaluation scale developed in this study is a customized tool that aligns with the characteristics of startups and plays a crucial role in securing sustainable competitiveness in rapidly changing market environments. Additionally, it offers practical guidance to support the successful growth of domestic startups and help them maintain their competitive edge in the market, contributing to the development of the startup ecosystem and the growth of the national economy.