• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.556-576
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• 2023

This study attempted to find a way to utilize idle spaces in rural areas as a way to respond to rural extinction. Based on the keywords "startup," "youth start-up," and "youth start-up+rural," start-up+rural," the study sought to identify the perception of idle facilities in rural areas through the keywords "Idle facilities" and "closed schools." The study presented basic data for policy direction and plan search by reviewing frequency analysis, major keyword analysis, network analysis, emotional analysis, and domestic and foreign cases. As a result of the analysis, first, it was found that idle facilities and school closures are acting importantly as factors for regional regeneration. Second, in the case of youth startups in rural areas, it was found that not only education on agriculture but also problems for residence should be solved together. Third, in the case of young people, it was confirmed that it was necessary to establish digital utilization for agriculture by actively starting a business using digital. Finally, in order to attract young people and revitalize the region through best practices at home and abroad, policy measures that can serve as various platforms such as culture and education as well as startups should be presented in connection with local residents. These results are significant in that they presented implications for youth start-ups in rural areas by reviewing start-up recognition for the influx of young people as one of the alternatives for the use of idle facilities and regional regeneration, and if additional solutions are presented through field surveys, they can be used to set policy goals that fit the reality.

• Korea Trade Review
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• v.45 no.4
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• pp.137-152
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• 2020

The increase in national income, the expansion of transportation network, the increase in leisure time, and the influx of foreign tourists in the era of internationalization, the influx of the outside population of Jeju region increased rapidly until 2020. However, the corona 19 (Covid-19) incident that began in January 2020 has hit the entire industry, and the tourism industry in Jeju has also been greatly damaged. However, in the second half of 2020, with some calming of the Corona 19 situation and difficult to leave overseas, the number of visitors to Jeju Island is increasing again as Koreans choose Jeju Island as their domestic tourism. This study analyzed the capacity of Jeju's external population based on the Contingent Valuation Method, and based on this, attempted to suggest policy recommendations for Jeju. The size of accommodations such as the density of visitors, toilets, and rest areas were excluded from consideration, and the level of securing the parking lot already exceeded the capacity, and the rate of securing the parking lot was 93.4%. In the case of accommodation, the total number of available rooms is 88,691, even if one guest per room is assumed, which is 32,372,215 per year, which is sufficient in terms of visitor capacity. To analyze the aspects of psychological capacity, this study analyzed whether the residents are feeling psychological discomfort through three methods of road congestion, garbage disposal, and sewage treatment through Contingent Valuation Method. However, the inconvenience caused by the increase of visitors and the effect of continuous population influx is working in combination, and it has the limitation that the effects of these independent factors cannot be specifically separated. As a result of the study, discomfort has already been recognized in terms of psychological capacity among the factors of capacity, and it was estimated that a cost of about 45 billion won per year was incurred as a result of deriving psychological costs through Contingent Valuation Method. In the future, a policy review is needed to resolve or maintain the perception of this discomfort through continuous management. Accordingly, it is necessary to recognize that the increase of visitors leads to the psychological discomfort of the residents, and to seek a policy alternative that can simultaneously increase the number of visitors and the comfort of the residence.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.827-831
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• 2023

This study sought to verify and examine the factors that influence social capital on social economy awareness. To summarize the results of the study, the results are as follows. As a result of analyzing the impact of social capital on social economy awareness, norms (t=3.228, p<.01) and participation (t=3.057, p<.01) had a significant impact on social economy awareness. In other words, the better the norms are observed and the higher the participation, the more influence it has on social economy awareness. Based on these results, the following implications were drawn. First, 'norms' refer to rules or standards that community members must adhere to within society, and these norms play a role in regulating the behavior of individuals and promoting social cooperation. Through this, it is believed that a strong normative system can have a significant impact on the way people participate or cooperate in social economy activities. Second, 'participation' is an indicator of how actively an individual or group participates in social activities. People with high levels of participation through social capital will have a high level of understanding of social economy awareness and will be able to act effectively. These results show that in previous studies, the better the norms were observed and the more trust and networks were strengthened, the higher the awareness of social economy. In contrast, in this study, norms and trust were found to have an effect, but the network did not, showing a difference from the results of previous studies, so repeated research is expected to be necessary in the future. Considering the results above, strengthening and developing the elements of social capital such as norms and participation can have a positive impact on overall socioeconomic awareness and development.

• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.363-384
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• 2024

This study was conducted to examine the user's experiences with the public library in Daejeon using big data analysis, focusing on the text mining technique. To know this, first, the overall evaluation and perception of users about the public library in Daejeon were explored by collecting data on social media. Second, through analysis using online news articles, the pending issues that are being discussed socially were identified. As a result of the analysis, the proportion of users with children was first high. Next, it was found that topics through LDA analysis appeared in four categories: 'cultural event/program', 'data use', 'physical environment and facilities', and 'library service'. Finally, it was confirmed that keywords for the additional construction of libraries and complex cultural spaces and the establishment of a library cooperation system appeared at the core in the news article data. Based on this, it was proposed to build a library in consideration of regional balance and to create a social parenting community network through business agreements with childcare and childcare institutions. This will contribute to identifying the policy and social trends of public libraries in Daejeon and implementing data-based public library operations that reflect local community demands.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Cartoon and Animation Studies
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• s.33
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• pp.597-616
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• 2013

Character industry is not only knowledge-based industry chiefly with copyrights but also motive power for creative economy to take a role functionally over the fields of industries because it has industrial characteristic as complement product to promote sale value in manufacturing industry and service industry and increase profit on sales. Since 2003, the national policy related to character has aimed to maximize effect among connected industries, extend its business abroad, enforce copyrights through the improvement of marketing system, develop industrial infrastructure through raising quality of character products. With the result of this policy, the successful cases of connected contents have been crystallized and domestic character industry has stepped up methodically since 2007. It is needed to reset the scales of character industry and industrial stats because there are more know-how of self industry promotion and more related characters through strategy of market departmentalization starting with cartoon, animation, games, novels, movies and musicals. Especially, The Korea government set our target for 'Global Top Five Character Power' since 2009 and has started to carry out to find global star characters, support to establish network among connected industries, diversify promotion channels, and develop licensing business. Particularly, since 2013, There have been prospered the indoor character theme park with time management just like character experimental marketing or Kids cafes using characters, the demand market of digital character focusing on SNS emoticon, and the performance market for character musical consistently. Moreover, The domestic and foreign illegal black markets on off-line have been enlarged, so we need another policy alternative. To prepare for the era of exploding character demand market and diversifying platform, it is needed to set up a solid strategy that is required the elements of policy changes in character industry to vitalize character industry and support new character design and connected contents. the following shows that the elements of policy changes related to the existing policy, the current position of market. Nowadays, the elements of policy changes in domestic character industry are that variety of consumers in the digital character market according to platform diversification, Convergence contents of character goods for the Korean waves, legalization of the illegal black contents market, and controling the tendency of consumers in departmentalized market. This can help find the policy issue entirely deferent with the existing character powers like US, Japan or Europe. In its final analysis, the alternatives are the promotion of models with contract copyrights of domestic and foreign connected contents, the diversification of profit models of platform economy, the additive development of target market related to enlarging the Korean waves, and the strategy of character market for the age-specific tendency according to developing character demand market.

• International Commerce and Information Review
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• v.16 no.4
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• pp.409-437
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• 2014

The purpose of this study is to present the specific ways of successful localization by analyzing the success and failures case for localization within the framework of the strategic models through a theoretical background and strategic models of localization. The strategic models of localization are divided by management aspects such as the localization of product and sourcing, the localization of human resources, the localization of marketing, the localization of R&D, harmony with a local community and delegation of authority between headquarters and local subsidiaries. The results, by comparing and analyzing the success and failures case for localization of individual companies operating in India, indicate that in terms of localization of product and sourcing, there are successful companies which procure a components locally and produce a suitable model which local consumers prefer and the failed companies which can not meet local consumers' needs. In case of localization of human resources, most companies recognize the importance of this portion and make use of superior human resource aggressively through a related education. In case of localization of marketing, It is found that the successful companies perform pre-market research & management and build a effective marketing skills & after service network and select local business partner which has a technical skills and carry out a business activities, customer support, complaint handling with their own organization. In terms of localization of R&D, the successful major companies establish and operate R&D center to promote a suitable model for local customers. In part of harmony with a local community, it shows that companies which made a successful localization understand the cultural environment and contribute to the community through CSR. In aspect of delegation of authority between headquarters and local subsidiaries, it is found that most of Korean companies are very weak for this part. there is a tendency to be determined by the head office rather than local subsidiaries. Implication of this thesis is that Korean enterprises in India should carry forward localization of products and components, foster of local human resource who recognize management and system of company and take part in voluntary market strategy decision, wholly owned subsidiary, establishment and operation of R & D center, understanding of local culture and system, corporate social responsibility, autonomy in management.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.