• Management & Information Systems Review
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• v.37 no.1
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• pp.139-154
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• 2018

R programming is free and open source system associated with a rich and ever-growing set of libraries of functions developed and submitted by independent end-users. It is recognized as a popular tool for handling big data sets and analyzing them. Reflecting these characteristics, R has been gaining popularity from data analysts. However, the antecedents of R technology acceptance has not been studied yet. In this study we identify and investigates cognitive factors contributing to build user acceptance toward R in education environment. We extend the existing technology acceptance model by incorporating social norms and software capability. It was found that the factors of subjective norm, perceived usefulness, ease of use affect positively on the intention of acceptance R programming. In addition, perceived usefulness is related to subjective norms, perceived ease of use, and software capability. The main difference of this research from the previous ones is that the target system is not a stand-alone. In addition, the system is not static in the sense that the system is not a final version. Instead, R system is evolving and open source system. We applied the Technology Acceptance Model (TAM) to the target system which is a platform where diverse applications such as statistical, big data analyses, and visual rendering can be performed. The model presented in this work can be useful for both colleges that plan to invest in new statistical software and for companies that need to pursue future installations of new technologies. In addition, we identified a modified version of the TAM model which is extended by the constructs such as subjective norm and software capability to the original TAM model. However one of the weak aspects that might inhibit the reliability and validity of the model is that small number of sample size.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.191-200
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• 2020

Companies are seeking to maximize profits through exports and imports in the ultra-fast, ultra-high-speed modern society. It is only possible to sustain its survival if it targets the global market, not based on any specific region. The K-POP group is also targeting overseas markets in a manner similar to the various global strategies used when companies make inroads into foreign markets, including exports, contracts and direct investment. The K-POP group is engaged in various forms of activities, ranging from simple forms of performance (export) that are visited and staged by an invitation from a certain foreign country to series performances (license) by an invitation from a local promoter and tour performances using its capabilities. The K-POP group is seeking to go beyond the art of single-stage performances and make a systematic plan and make inroads into foreign countries in the form of direct investment suitable for each foreign country. The K-POP group made inroads into overseas markets in the form of simple performances from the late 1990s to 2005, when 'Korean Wave' was first introduced. Group H.O.T., etc. are typical examples. Since then, it has sought to enter overseas markets in the form of franchises by accepting overseas members by 2018, starting with Super Junior in 2005. Since then, the K-POP group in the form of joint investment attempted as group IZ*ONE in 2018 appeared, and a voice story came out in September 2018 when South Korea's JYP Entertainment and Tencent of China joined forces. Unlike K-POP Group, which has entered foreign markets with a global strategy based on the existing export method (H.O.T.), 'Boystory' is a representative group that is made with joint investment, which is a direct investment method. In February 2020, RBW released 'D1Verse,' a five-member group selected by Vietnam's reality show, as a joint investment-type group. This shows the possibility that domestic and foreign companies will release a group in the form of joint investment in order to pursue both globalization and localization.

• Journal of Service Research and Studies
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• v.12 no.4
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• pp.1-18
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• 2022

This study was conducted to prove that the service philosophy is the development principle of human society in the service age. From ancient times to the present, the service philosophy was tried to show the wisdom of the development of human society in all earth spaces including the East and the West. In addition, it tried to prove that the service philosophy was at the center of the development wisdom of many countries and individuals who flickered on all space on earth and all human time. The study showed that the differences between countries were in software rather than hardware. Furthermore, it was analyzed that countries with a service philosophy embedded in the center of software such as spirit and culture made a great contribution to human society. The cases of Greece and Rome, the Republic of Venice, the Republic of the Netherlands, followed by the United States and modern Korea prove this, and the Soviet Union can be seen to disprove it. The former was a society in which state-run software was strong, and the latter was a society in which hardware was strong. There is a big difference between the case of the state, which citizens have autonomously organized and operated, and the case of the upper-level state-led operation. Since the leadership of the upper classes is not based on the service philosophy, the accumulated software power is weak, so it can be said that the accumulation of wisdom in human society is weak. Therefore, while the essence of human society so far has been a society of self-centered animal ecosystems led by selfishness, the human society in the service age from now on can be said to be a society of plant ecosystems where mutual respect and self-centeredness coexist. Just as the society centered on the service philosophy in the past human society prospered and left a greater legacy to mankind, it is suggested that the human society in the future service era should be a human society of a plant ecosystem centered on the service philosophy. Further in-depth studies related to this are needed in the future.

• The Korean Journal of Franchise Management
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• v.4 no.1
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• pp.1-24
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• 2013

The purpose of this work is to analyze the case that Yeongju Municipality Government of Gyeongbuk and Jung Donuts Co., Ltd., a franchise company, activated local economy through Farm's Franchise Commercialization (FCC) and to establish the concept of FCC. As a food service franchise company, Jung Donuts specializes in making and selling glutinous rice donuts. In cooperation with Commodity Dept. of Yeongju Municipality Government, the company cultivates raw materials under contract. Such farm's franchise business has the following advantages: First, it is meaningful in the point that it contributes to opening a new market of local agricultural products in a stable way. In short, by using most agricultural products of Yeongju as raw materials, the business serves as a stable market for farmers. Secondly, it is possible to set a reasonable price through stable supply of raw materials. It is to advantages of both producers and consumers. Thirdly, the business brings about the effect of employment rise. It can cause increases in employment of franchise head office and its agencies, and of the Commodity Dept.. Lastly, it produces the promotion effect of local special products. By expanding its agencies across the country, a franchise business can promote the items of raw materials in terms of marketing. The successful FCC needs to meet three requirements as follows. The first one is to establish systematic logistic system. Stable logistic system is required in order to directly distribute and deliver products to nationwide agencies by a producing place. The second one is constant R&D activity. Through the activity of R&D of raw materials and equipment, they should be used most effectively. The third one is to build mutual trust relationship. For long-term business achievements, it is required to establish mutual trust relationship in which relevant entities share their visions with each other and cooperate with each other.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.17-32
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• 2023

The entrepreneurial activities of college students play a significant role in modern economic and social development, particularly as a solution to the changing economic landscape and youth unemployment issues. Introducing innovative ideas and technologies into the market through entrepreneurship can contribute to sustainable economic growth and social value. Additionally, the entrepreneurial intentions of college students are shaped by various factors, making it crucial to deeply understand and appropriately support these elements. To this end, this study systematically explores the importance and impact of role models through a multiple serial mediation analysis. Through a survey of 300 college students, the study analyzed how two psychological variables, growth mindset and entrepreneurial self-efficacy, mediate the influence of role models on entrepreneurial intentions. The presence and success stories of role models were found to enhance the growth mindset of college students, which in turn boosts their entrepreneurial self-efficacy and ultimately strengthens their entrepreneurial intentions. The analysis revealed that exposure to role models significantly influences the formation of a growth mindset among college students. This mindset fosters a positive attitude towards viewing challenges and failures in entrepreneurship as learning opportunities. Such a mindset further enhances entrepreneurial self-efficacy, thereby strengthening the intention to engage in entrepreneurial activities. This research offers insights by integrating various theories, such as mindset theory and social learning theory, to deeply understand the complex process of forming entrepreneurial intentions. Practically, this study provides important guidelines for the design and implementation of college entrepreneurship education. Utilizing role models can significantly enhance students' entrepreneurial intentions, and educational programs can strengthen students' growth mindset and entrepreneurial self-efficacy by sharing entrepreneurial experiences and knowledge through role models. In conclusion, this study provides a systematic and empirical analysis of the various factors and their complex interactions that impact the entrepreneurial intentions of college students. It confirms that psychological factors like growth mindset and entrepreneurial self-efficacy play a significant role in shaping entrepreneurial intentions, beyond mere information or technical education. This research emphasizes that these psychological factors should be comprehensively considered when developing and implementing policies and programs related to college entrepreneurship education.

• Information Systems Review
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• v.12 no.3
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• pp.19-48
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• 2010

This study focused on characterizing various factors in order for learners to maintain their interests in learning and to maximize learning effects as the top priority purpose of university e-Learning, on the basis of results of conceptual studies on existing e-Learning and practical studies, and then on examining them practically. It also analyzed which factors would have greater influence on learning effects of e-Learning in general. Moreover, in comparison with existing numerous studies which examined only factor such as learning effects of e-Learning, it analyzed such things in detail according to division into three items such as learning satisfaction, learning transfer and learning recommendation. To achieve such purposes of the study, it characterized and set 3 factors such as learning contents, instructional design and user convenience on the assumption that such factors have a significant influence on learning effects of e-Learning. Moreover, the factor of learning contents includes 3 detailed elements, i.e., learning issue and objective, knowledge information, and consistency and propriety, and the factor of instructional design includes 4 detailed elements, i.e., interest and sympathy, interaction, contents presentation and explanatory strategy. Lastly, the factor of user convenience includes 2 detailed elements such as screen configuration, and check-up of contents and teaching schedule. According to analytical results, it showed all 3 factors such as learning contents, instructional design and user convenience have a significant influence on learning effects of e-Learning(i.e., learning satisfaction, learning transfer and learning recommendation). In more detail, it showed the learning issue and objective from the factor of learning contents have the greatest influence on learning satisfaction of e-Learning. Then, it is the most important to set the learning issue and objective with given priority to learners and set the learning objective estimable, in order to raise the learning satisfaction. It showed the contents presentation from the factor of instructional design on the learning transfer. Therefore, it is the most important to structuralize mutual relation and presentation orders to promote learning systematically and to let learners access to such things, for the purpose of raising the learning transfer. Moreover, it showed the interest and sympathy from the factor of instructional design has the greatest influence on the learning recommendation. Thus, it is the most important to promote learners' interests to the maximum using well-timed media, and to give a lecture enough to arouse learners' sympathy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.132-140
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• 2005

This study was conducted to develop regional site index equations for main tree species in Gangwon, Gyunggi-Chungcheong, Gyungsang, and Jeolla area of Korea, using environmental and soil factors obtained from a digital forest site map. Using the large data set obtained from the digital forest map, a total of 28 environmental and soil factors were regressed on site index by tree species for developing the best site index equations for each of the regions. The selected main tree species were Larix 1eptolepis, Pinus koraiensis, Pinus densiflora, Pinus thunbergii, and Quercus acutissima. Finally, four to five environmental and soil factors by species were chosen as independent variables in defining the best regional site index equations with the highest coefficients of determination $(R^2)$. For those site index equations, three evaluation statistics such as mean difference, standard deviation of difference and standard error of difference were applied to the data sets independently collected from fields within the region. According to the evaluation statistics, it was found that the regional site index equations by species developed in this study conformed well to the independent data set, having relatively low bias and variation. It was concluded that the regional site index equations by species had sufficient capability for the estimation of site productivity.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.