• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.155-175
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• 2016

The purpose of this paper is to find the success factors for a newly developed marine financial city of Busan. For this, in the paper, we did case studies about advanced marine financial cities such as Hamburg, Oslo, Pireus, Shanghai, Singapore and London. And we made the hierarchical questionnaire for this study from literature reviews and interviews with experts in the field of the marine finance. A hierarchy was made up of 4 factors as the first class and the each factor consisted of four elements as the second class. The results of AHP analysis are as follows. First, the most important factor in the priorities with respect to marine financial city of Busan is "development of marine finance(0.371)" within the four factors as the first class. Next came "business environment(0.248)", "infrastructure(0.206)" and "support of government(0.175)". Second, the most important things is a clustering for marine finance(0.134) within 16 elements as the second class. We also analysed the priorities by the each factor of the second class. The most important element is an industrial clustering of marine finance(0.400) for "development of marine finance" and a clustering of the shipbuilding & marine industry(0.175) for "business environment" factor. And the ICT & transportation(0.326) is the most important element for "infrastructure" and a support of the national government(0.423) for "support of government" factor.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.353-376
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• 2023

The global pandemic and the development of virtual and augmented reality technologies have led a metaverse boom that enables a lot of interactions in virtual worlds, and is being utilized in various fields such as business, government, and education etc. Despite the growing interest in the metaverse, its scope and definition are still unclear and the concept is still evolving, making it challenging to establish its governance. Governmental entities are also investing intensively in public metaverses to make public value and promote social welfare, but they are underutilized due to lack of systematic governance. Therefore, in this study, we propose a public metaverse governance framework and identify the relative importance of the factors. Furthermore, since a public metaverse should be accessible to anyone who wants to use, we explore the factors of shadow work and examine the ways to minimize it. Based on the socio-technical system theory, we derived public metaverse governance factors from previous literature and topic modeling and then generate a framework with 23 factors through expert interviews. We then tested relative priority of the factors using the analytic hierarchical process (AHP) from the experts. As a result, the top five overall rankings are: 'roles and responsibilities', 'standardization/modularization', 'collaboration and communication', 'law and policies', and 'availability/accessibility'. The academic implications of this study are that it provides a comprehensive framework for public metaverse governance, and then the practical implications include suggesting prioritized considerations for metaverse operations in the public sector.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.1-11
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• 2018

Recently ICT, new technologies such as IoT, Cloud, and Artificial Intelligence are changing the information society explosively. But personal information leakage incidents of consignee's company are increasing more and more because of the expansion of consignment business and the latest threats such as Ransomware and APT. Therefore, in order to strengthen the security of consignee's company, this study derived the checklists through the analysis of the status such as the feature of consignment and the security standard management system and precedent research. It also analyzed laws related to consignment. Finally we found out the relative importance of checklists after it was applied to proposed AHP(Analytic Hierarchy Process) Model. Relative importance was ranked as establishment of an internal administration plan, privacy cryptography, life cycle, access authority management and so on. The purpose of this study is to reduce the risk of leakage of customer information and improve the level of personal information protection management of the consignee by deriving the check items required in handling personal information of consignee and demonstrating the model. If the inspection activities are performed considering the relative importance of the checklist items, the effectiveness of the input time and cost will be enhanced.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• 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.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.23-38
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• 2012

Massive Multiplayer Online Role Playing Games(MMORPGs) headed by some Korean game companies such as NC Soft, NHN, and Nexon have exploded in recent years. However, it becomes one of the major challenges for the MMORPG developers to design their games to appeal to gamers since only a few MMORPGs succeed whereas they require a huge amount of initial investment. Under this background, our study derives the major elements for designing MMORPG from the literature, and identifies the ones critical to the users' satisfaction and their willingness to pay among the derived elements. Though most previous studies on the design elements of MMORPG have used analytic hierarchy process(AHP), our study adopts artificial neural network(ANN) as the tool for identifying key success factors in designing MMORPG. The results of our study show that the elements of the game contents quality have a bigger effect on the user's satisfaction, whereas the ones of the value-added systems have a bigger effect on the user's willingness to pay. They also show that user interface affects both the user's satisfaction and willingness to pay most. These results imply that the strategies for the development of MMORPG should be aligned with its goal and market penetration strategy. They also imply that the satisfaction and revenue generation from MMORPG cannot be achieved without convenient and easy control environment. It is expected that the new findings of our study would be useful forthe developers or publishers of MMORPGs to build their own business strategies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.1-13
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• 2017

The purpose of this study is to find out which factors determine the success of start-up in the initial market and what are the most important determinants. For the empirical analysis, the questionnaire related to the analysis of success factors for start-up success was designed according to the quantitative analysis (AHP technique). First, we selected 8 representative success factors for successful start-up in the initial market. In order to determine the degree of priority among these factors, we surveyed 12 entrepreneurs who are interested in entrepreneurship, universities, research institutes, and public officials. As a result of the empirical analysis, 51% of the funds in the tier 1 were ranked as the top priority to determine success factors. Followed by research and development (32.5%), management (8.7%) and marketing (7.8%). In particular, when each of the four items is calculated as 100 according to the result of the tier 1, and the tier 2 is converted, the foreign investment is analyzed as 43.7%. It was followed by 15.14% of R & D facilities, 14.07% of ideas, 8.7% of managerial ability, 7.29% of domestic investment, 5.85% of buyer feedback, 3.3% of development strategy and 1.95% of marketing strategy. Among the eight success factors, overseas investment items showed the closest preference to half, and it was the most important variable that determines the success or failure of market entry. The implication of this study is that many start-ups in Korea expect to receive investment and support from overseas accelerators. This means that overseas investment itself has been recognized as a start-up that makes services and products that can be used in the global market. A high preference for attracting foreign investment is due to the fact that the amount of investment is larger than that of Korea and that it can flexibly cope with the pressure on the performance compared to domestic investors. In this study, it was meaningful that we could confirm this fact through questionnaires of start-up experts. In future research, we need to find a viable alternative through studying how to provide start-up to foreign direct investment at the national level.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.123-131
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• 2015

Recently the development of ICT has a big impact on education field, and diffusion of smart devices has brought new education paradigm. Since people has an opportunity to use various contents anytime and communicate in an interactive way, the method of learning has changing. In 2011, Korean government has established the smart education promotion plan to be a first mover in the paradigm shift from e-learning to smart learning. Especially, government aimed to improve the quality of learning materials and method in public schools, and also to decrease the high expenditure on private education. However, the achievement of smart education policy has not emerged yet, and the refinement of smart learning policy and strategy is essential at this moment. Therefore, the purpose of this study is to propose the successful strategies for smart learning in public education. First, this study explores the status of public education and smart learning environment in Korea. Then, it derives the key success factors through SWOT(Strength, Weakness, Opportunity, Threat) analysis, and suggests strategic priorities through AHP(Analytic Hierarchy Priority) method. The interview and survey were conducted with total 20 teachers, who works in public schools. As a results, focusing on weakness-threat(WT) strategy is the most prior goal for public education, to activate the smart learning. As sub-factors, promoting the education programs for teachers($W_2$), which is still a weakness, appeared as the most important factor to be improved. The second sub-factor with high priority was an efficient optimizing the capability of new learning method($S_4$), which is a strength of systematic public education environment. The third sub-factor with high priority was the extension of limited government support($T_4$), which could be a threat to other public schools with no financial support. In other words, the results implicate that government institution factors should be considered with high priority to make invisible achievement in smart learning. This study is significant as an initial approach with strategic perspective for public education. While the limitation of this study is that survey and interview were conducted with only teachers. Accordingly, the future study needs to be analyzed in effectiveness and feasibility, by considering perspectives from field experts and policy makers.

• Journal of Agricultural Extension & Community Development
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• v.2 no.2
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• pp.205-227
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• 1995

The objectives of this study were to 1) classify the cooperative farmers' organizations in Korea according to the development level and institutional aspects through the exploration of its' conceptual and institutional basis, 2) analyze the farmers' needs for organization, 3) identify the problems and situation of organizations, and 4) formulate an effective management model for each cooperative farmers' organization. The study was carried out through a review of literature and using available statistical data collected from various sources and empirical survey. Major findings of the study were: 1) the cooperative farmers' organizations could be classified into four types : crop units, farming cooperative corporation, trust farming companies and joint-stock agri-business. 2) a lot of members of the organization feel that the information is insufficient, the opportunity to suggest their own ideas is hardly given, and the members are not satisfied with the cooperation among the members, 3) the members who have higher level of schooling education showed a higher participation level in the organization, 4) most of members did not recognize the organization they participated in, 5) participation of the organization's members and concerned institutions is an important factor to promote problem solving and better communication within the organization, 6) any type of continuing education for the members is needed to facilitate the transfer of a new agricultural and organizational technology, 7) research and development(R & D) is one of the most important factors of the development of organizations, 8) most organizations are deficient in professional management skills(financial, personal, accounts, etc.), 9) the trust farming companies have difficulties in managing the firm on account of the characteristics of agriculture(especially seasonal), the dispersed trust lands, and the need for more alternative work in the winter season, and 10) in the case of agri-businesses, their organizations are more specialized in marketing and have more structured systems of management. Based on the results of the study the following recommendations were made for further improvement and development of agricultural cooperative organizations : (1) More governmental support should be given to education for improvement of the organizational structure. And more deliberate and differentiated governmental support should be provided for the organizations to be viably managed. (2) For more efficient communication between the members and the organization, more opportunities for discussion are needed. (3) The more research should be committed to this kind of work in order to get more analytic data and strategic plans of cooperative organizations.

• Culinary science and hospitality research
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• v.22 no.4
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• pp.81-94
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• 2016

The purpose of this study is to measure the relative importance of the factors that affect school to work transition that food service-related majors and workers recognize. To this end, this study composed such factors into a second hierarchy level of individual background, educational background, and preparation effort to enter labor market. The study made us of the analytic hierarchy process(AHP), which calculates the importance of each factor through the relative evaluation of each factor in the hierarchy. The results of analysis are as follows. First, in the second hierarchy level, effort to enter the labor market exhibited the highest relative importance. In the case of four-year college students, educational background had the highest relative importance. Second, in case of third hierarchy level factors relating to personal background, gender had the highest relative importance. As for educational background, the type of college had the highest relative importance. As to the effort to enter labor market, overseas working experience while in college and job searching channels had high relative importance, while vocational training experience had the lowest relative importance. Third, the analysis result of complex weighted value showed that the type of college had the highest complex weighted value. In future studies, the type of businesses and business conditions in the food service industry should be subdivided for an analysis of influential factors, and, based on this, customized career guidance should be made for specific career paths of each student.