• Industry Promotion Research
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• v.9 no.3
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• pp.273-284
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• 2024

The objective of this thesis is to analyze the impact of transactional and transformational leadership styles on job performance. This research employs questionnaire surveys and statistical analysis to examine the relationships among the three variables. The subjects of this thesis are bank employees, and the survey was conducted using a random sampling method via online questionnaires. Data was statistically analyzed using SPSS 28.0, which included frequency analysis, reliability and validity analysis, correlation analysis, and regression analysis. The findings indicate that transformational leadership can significantly enhance job performance by encouraging innovation and boosting employee morale. Conversely, transactional leadership, with its excessive emphasis on rules and procedures and a strict reward and punishment system, may limit employees' innovative capabilities and reduce their satisfaction, thus negatively affecting job performance. This thesis contributes to understanding the impact of leadership styles on organizational effectiveness, advancing leadership theories, and providing theoretical support for organizational management decisions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.33-46
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• 2017

In Korea, small and medium sized domestic enterprises (SMEs) play an pivotal role in the national economy, accounting for 99.8% of all enterprises, 87.9% of total employment, and 48.3% of production. and SMEs were driving a real force of the development of national economy in many respects such as innovation, job creation, industrial diversity, balanced regional development. Despite their crucial role in the national development, most of SMEs suffer from a lack of R&D capabilities and equipments as well as funding capacity. Government-Supported Research Institute (GRI) can provide SMEs with valuable supplementary technological knowledges and help them build technological capacities. so, In order to effectively support SMEs, government and GRI must be a priority to know about the factors influencing the performance related to technology transfer and technological collaborations. The paper analyzes the effects of Korean IT SMEs Venture external collaborations and technology transfer on their performances, according to their collaboration activities and technology transfer experiences. The results show that there was a significant difference between '3~5times' of technology transfer experience and 'zero technology transfer experience' in the case of technology transfer experience. In case of technological collaboration type, there was a significant difference between 'R&D manpower' and 'enhancement of technological capabilities including core technologies'. The results show that the effectiveness of technology transfer of Korean IT SMEs Venture depends on experiences, types of collaboration activities. so the results of this research will be useful for Government-Supported research institute (GRI)' policy makers when establishing technology commercialization support policies and strategic planning of small and medium sized domestic enterprises.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2769-2777
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• 2014

This research herein aims to assist maximizing performance of introduced systems by managing the acceptance factor of uTradeHub that is easily overlooked in the system installation process by trading companies aiming to install uTradeHub system in future by conducting the two following things: i) grasping factors affecting system usefulness and user satisfaction derived from uTradeHub acceptance factor, and ii) analyzing the effect relationship of system usefulness on user satisfaction at an inspection level of system usefulness and user satisfaction, since the uTradeHub system introduced for mid- and small-sized trading companies in export and import works in mid-2008. Proof analysis was conducted by using SPSS 19.0 statistic package on data of 112 effective responses collected through questionnaire surveys, whose results are as follows. First, the uTradeHub acceptance factors having a significant effect on system usefulness are relative advantage, easy of use, task adaptedness, support of CEO, maturity of IT infrastructures, and degree of education/training. Second, the uTradeHub acceptance factors having an effect on user satisfaction are relative advantages, task adaptedness, support of CEO, maturity of IT infrastructures, and degree of education/training. Third, system usefulness showed a significant effect on user satisfaction.

• International Area Studies Review
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• v.21 no.1
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• pp.119-136
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• 2017

This study investigates the moderating effects of corporate mentoring on the relationship between entrepreneurship and start-up satisfaction. The research was designed to examine a direct relationship between the entrepreneurship on the start-up satisfaction. In addition, indirect impacts of corporate mentoring on the relationship between entrepreneurship and start-up satisfaction. Data was collected by distributing questionnaires through e-mail or direct visit to the participating companies in the Start-up Leading University Project, college start-up clubs, companies in university business incubating centers, and general entrepreneurs in Busan and Gyeongnam areas. Empirical data was then analyzed using the SPSS 18.0 statistical program. The results reveal that such categories as innovation and autonomy in entrepreneurship have a direct positive effect on entrepreneurial satisfaction and the corporate mentoring has a moderating effect only when autonomy has a direct positive effect on start-up satisfaction. It is implied that an improvement of start-up satisfaction may be attained only when mentoring programs are well accepted by entrepreneurs and their workers while working in a free and autonomous environment with a flexible corporate culture. This study may provide the theoretical and practical implications for start-up support organizations such as start-up support groups of Start-Up Leading University Project.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.71-94
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• 2020

Korea's innovation cluster policy has been implemented since 2005 with the goal of balanced regional development. The purpose of this study is to investigate the factors affecting the sustainability of innovative cluster tenants by using Pangyo Techno Valley as an example. Pangyo Techno Valley was established under the leadership of the local government (Gyeonggi-do) rather than the central government and it is called "Silicon Valley of Korea" and "Asia Silicon Valley" and is becoming more representative. The growing number of companies in Pangyo Techno Valley decreased in 2017 compared to 2016. This is because Pangyo Techno Valley's business ecosystem will change from 2019. In this paper, quantitative and qualitative studies were conducted to investigate the influencing factors. Quantitative research was conducted based on the survey and qualitative research was applied through interviews. The quantitative research examined the factors affecting the sustainability of Pangyo Techno Valley, and the qualitative research examined the specific reasons and additional factors for the quantitative research results. The quantitative results showed that factors affecting sustainability in terms of changes in corporate internal conditions, human and physical infrastructure, cooperation and synergy, and occupancy patterns. The specific reason for the impact appeared in the qualitative research process. The support category of local governments did not show any significant factors in quantitative research. In addition, qualitative research suggested 'Good image of Pangyo Techno Valley' as the category that has the greatest impact on sustainability. It is shown that companies are passive and expect the role of local governments in activating cooperation network in Pangyo Techno Valley. In this paper, based on the results of the study, Pangyo Techno Valley is presented with a realistic plan based on real estate issues and an ideal plan with a long-term perspective.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.19-34
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• 2021

The government budget for promoting startup have been skyrocketed as catching up with increasing demands for high-tech startup by disruptive innovation resulted from rapid technology change. However, major trend of startup have still fallen on self-employed type of startup due to the lack of expertise and fund in spite of desperate government policy efforts. In reality, the access to high-tech startup has been very limited and too high huddle to would-be entrepreneur. This paper implement empirical analysis on the effects of entrepreneur competency and satisfaction level to government support, considering these as the KSF for the growth and success of high-tech startup, to the performance of the company. In particular, it focus on defining unique characteristics of high-tech startup through differential proving by the backgrounds of entrepreneur such as major, R&D experience, patent possession, CTO possession. This research carry out survey to 217 entrepreneurs in high-tech company in Daejon and Daegue at R&D Special Innopolis Zone. Research results are as follow. First, entrepreneurial achievement competencies, conceptualization competencies, network competencies and market recognition competencies positively affect the financial and non-financial performance and organizational and technical competencies, while organizational and technological competencies only positively impact on non-financial performance. Second, the satisfaction level of government support showed a positive moderating effect on entrepreneurial achievement competencies and financial performance, while no significant effect in other competencies. Third, positive differential effect by the technological background of entrepreneur such as Major, R&D experience, patent possession, CTO possession) have been confirmed. This paper deliver several significant implications and contributions, First, it propose classified and systematized entrepreneur competency through the domestic and foreign literature reviews. Second, it proves the need for the wider spread of team based startup culture rather then sole startup. Third, it also proves the important role of technological background of entrepreneur among the characteristics of high-tech startup.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.247-259
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• 2021

This study aimed to look for educational ways to prepare for the future society for education and people of talent who will lead the post-COVID-19 era. To this end, the factors necessary for the type of future talent in the post-COVID-19 era were identified by analyzing Big data. Based on the deducted factors composing the type of talent in the post-COVID-19 era, policy direction according to the emergence of the post-COVID-19 era were deducted through the interviews with the group of experts and delphi survey, and on the basis of this, this study sought for"a plan for the educational change in line with cultivation of people of talent in the post-COVID-19 era. The results of this study are as follows. First, through the big data analytics and analysis of the interviews, convergence, ICT utilization ability, creativity, self-regulated competency and leadership were found to be the factors necessary for the type of talent in the post-COVID-19 era. Second, it considered the innovation of digital education system and the support for vulnerable classes as the issue for cultivation of people of talent in the post-COVID-19 era. Third, the most important policy with regard to the educational direction for cultivation of people of talent in the post-COVID-19 era was cultivation of convergence talents. Convergence is a very important variable in the post-COVID-19 era since it creates new values by connecting things that are separated from each other. Hopefully, this study will build a basis for competency development, education and training in preparation for the post-COVID-19 era.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Safety and Health at Work
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• v.10 no.2
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• pp.229-236
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• 2019

Background: Emerging reports suggest the potential for adverse health effects from exposure to emissions from some additive manufacturing (AM) processes. There is a paucity of real-world data on emissions from AM machines in industrial workplaces and personal exposures among AM operators. Methods: Airborne particle and organic chemical emissions and personal exposures were characterized using real-time and time-integrated sampling techniques in four manufacturing facilities using industrial-scale material extrusion and material jetting AM processes. Results: Using a condensation nuclei counter, number-based particle emission rates (ERs) (number/min) from material extrusion AM machines ranged from $4.1{\times}10^{10}$ (Ultem filament) to $2.2{\times}10^{11}$ [acrylonitrile butadiene styrene and polycarbonate filaments). For these same machines, total volatile organic compound ERs (${\mu}g/min$) ranged from $1.9{\times}10^4$ (acrylonitrile butadiene styrene and polycarbonate) to $9.4{\times}10^4$ (Ultem). For the material jetting machines, the number-based particle ER was higher when the lid was open ($2.3{\times}10^{10}number/min$) than when the lid was closed ($1.5-5.5{\times}10^9number/min$); total volatile organic compound ERs were similar regardless of the lid position. Low levels of acetone, benzene, toluene, and m,p-xylene were common to both AM processes. Carbonyl compounds were detected; however, none were specifically attributed to the AM processes. Personal exposures to metals (aluminum and iron) and eight volatile organic compounds were all below National Institute for Occupational Safety and Health (NIOSH)-recommended exposure levels. Conclusion: Industrial-scale AM machines using thermoplastics and resins released particles and organic vapors into workplace air. More research is needed to understand factors influencing real-world industrial-scale AM process emissions and exposures.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.