• Journal of vocational education research
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• v.37 no.3
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• pp.85-111
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• 2018

The purpose of this study is to derive the competence of nursing teacher in Specialized vocational high school. The knowledge, skills, and attitudes required for the nursing teachers were verified and the competency groups and sub-competencies were verified and priorities were suggested. To do this, 23 nursing teachers who were currently working in the Specialized vocational high school were selected as expert panels and the study was conducted using Delphi and Layered Analysis(AHP) technique. The results of this study were as follows. First, the competency group of the nursing teacher in Specialized vocational high school showed teaching and learning methods & techniques, student guidance, curriculum development and operation, school management support, industry-academy cooperation, self-development and professionalism improvement. The total number was 6. Second, the sub-competency is composed of knowledge and skills related to the curriculum, planning and preparation of instruction, instruction operation, guidance of experiment and practice, instruction evaluation, development and utilization of teaching and learning medium, instruction environment, guidance on student education activities outside the curriculum, career guidance, life guidance, class management, guidance of technology and qualification, development of school curriculum, organization and operation of school curriculum, planning and operation of field practice, work planning, school management support, teacher evaluation and personnel management support, leaning support(practice place and equipment), establishment and operation of industry-academia cooperation, strengthening community cooperation, public relations in the school(department), field research for improvement of educational activities, participation in conferences and training, exploration of new knowledge and technology, The total number was 26. The most important of the relative importance was the curriculum development and operation. The subordinate competencies that have a high priority in each competency group were guidance of experiment and practice, guidance of technology and qualification, planning and operation of field practice, leaning support(practice place and equipment), management and work planning, establishment and operation of industry-academia cooperation, exploration of new knowledge and technology. The competency system derived from this study will be applied to the training and evaluation of nursing teachers in the future and can be used as basic data for related research.

• Journal of Navigation and Port Research
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• v.46 no.1
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• pp.1-10
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• 2022

This study aims to investigate changes in the demand for ship officers in response to changes in the shipping industry environment in which Maritime Autonomous Surface Ships (MASS) emerge according to the application of the fourth industrial revolution technology to ships, and it looks into changes in the skill of ship officer. It also analyzes and proposes a plan for nurturing ship officers accordingly. As a result of the degree of recognition and AHP analysis, this study suggests that a new training system is required because the current training and education system may cover the job competencies of emergency response, caution and danger navigation, general sailing, cargo handling, seaworthiness maintenance, emergency response, and ship maintenance and management, but tasks such as remote control, monitoring diagnosis, device management capability, and big data analysis require competency for unmanned and shore-based control. By evaluating the importance of change factors in the duties of ship officers in Maritime Autonomous Surface Ships, this study provides information on ship officer educational institutions' response strategies for nurturing ship officers and prioritization of resource allocation, etc. The importance of these factors was compared and evaluated to suggest changes in the duties of ship officers and methods of nurturing ship officers according to the introduction of Maritime Autonomous Surface Ships. It is expected that the findings of this study will be meaningful as it systematically derives the duties and competency factors of ship officers of Maritime Autonomous Surface Ships from a practical point of view and analyzed the perception level of each relevant expert to diagnose expert-level responses to the introduction of Maritime Autonomous Surface Ships.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.109-124
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• 2024

The value of platform companies is rapidly increasing, exerting significant influence across industries. Identifying and fostering promising platform companies is crucial for enhancing national competitiveness. Consequently, tailored evaluation standards are necessary for such companies. This study derived investment decision factors specific to platform companies and compared the importance of each factor using Analytic Hierarchy Process (AHP) analysis. Key factors included platform characteristics, finance, entrepreneur (team), market, and product/service attributes. The findings revealed that platform characteristics were deemed the most crucial factor for investors. Specifically, factors such as platform size, ease of value fixation, core participant group, and data value were identified as pertinent for evaluating platform companies. Moreover, analysis distinguished between investors with prior platform investment experience and those without. Significantly, investors with platform investment experience placed greater emphasis on the value of data secured by platform Furthermore, it was observed that investors prioritized future value and growth potential over current value when investing in platform. Notably, founder/team characteristics, typically highly regarded in previous studies, ranked lower in importance in this study, highlighting a shift in focus. The discrepancy between this study's results and prior research on investment decision factors is attributed to the specificity of the questions posed. By focusing on investment decision factors for platform startups rather than generic startup inquiries, investor responses aligned more closely with platform-focused considerations. Given the burgeoning venture investment landscape, there's a growing need for detailed research on startups within specific sectors like IT, travel, and biotech. This approach can replace extensive research covering all startup types to identify investment decision factors suited to the characteristics of each individual industry.

• 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.

• Journal of Environmental Policy
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• v.13 no.1
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• pp.69-93
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• 2014

The aim of this research was to develop a climate change vulnerability index at the district level (Si, Gun, Gu) with respect to the health care sector in Korea. The climate change vulnerability index was esimated based on the four major causes of climate-related illnesses : vector, flood, heat waves, and air pollution/allergies. The vulnerability assessment framework consists of six layers, all of which are based on the IPCC vulnerability concepts (exposure, sensitivity, and adaptive capacity) and the pathway of direct and indirect impacts of climate change modulators on health. We collected proxy variables based on the conceptual framework of climate change vulnerability. Data were standardized using the min-max normalization method. We applied the analytic hierarchy process (AHP) weight and aggregated the variables using the non-compensatory multi-criteria approach. To verify the index, sensitivity analysis was conducted by using another aggregation method (geometric transformation method, which was applied to the index of multiple deprivation in the UK) and weight, calculated by the Budget Allocation method. The results showed that it would be possible to identify the vulnerable areas by applying the developed climate change vulnerability assessment index. The climate change vulnerability index could then be used as a valuable tool in setting climate change adaptation policies in the health care sector.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.25-38
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• 2020

The objective of this study is to propose a direction for creating a memorial park for the 250 students victims of the Sewol ferry disaster. To this end, this study first attempted to understand the matters discussed at various levels to create a memorial park and find a way that the park can be built by gathering opinions from the bereaved families and the victims themselves, as well as local residents, and experts. Workshops, competitions, special lectures, and websites, etc, were analyzed. A social discourse analysis methodology was used for systematic analysis, and the analyzed discourse was categorized into 4 types for assessment, and the functions and roles were subdivided into 15 types. To assess the priorities and the adequacy of the discourse, an analytic hierarchy process (AHP) was used among 30 activists, public servants, and experts. Then, a survey was conducted to analyze the perception of the residents (467 participants including the bereaved families) about the memorial park. Based on the results of the analysis, two directions were set for the memorial park. First, is a memorial park to remember the victims in everyday life. It must be a park with various cultural contents instead of a conventional memorial park that is solemn and grave sharing anguish and sorrow. The memorial park for the Sewol ferry disaster must become a space where visitors can naturally encounter and remember the victims. Second, is a park that serves as a catalyst that brings change and innovation to the community. It must be able to bring change to the community with direct and indirect influence. It must serve as an impetus to bring change and innovation to the community in the mid-to-long-term. Having many visitors may also lead to an economic effect. These visitors may not just stay in the park, but even contribute to revitalizing the local businesses. The purpose of this study is to apply the research findings to guide the International Design Competition scheduled for 2020 and serve to establish guidelines for a continuous park management system.

• Korean Journal of Construction Engineering and Management
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• v.17 no.4
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• pp.103-115
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• 2016

The 106 buildings of 200 meters' height or greater were completed around the world in 2015 (CTBUH, The Council on Tall Buildings and Urban Habitat). They beat every previous year on record, including the previous record high of 99 completions in 2014. This brings the total number of 200-meter-plus buildings in the world to 1,040, exceeding 1,000 for the first time in history and marking a 392% increase from the year 2000, when only 265 existed. South Korea recorded three completions during 2015 - improving slightly over 2014, in which it had one. This study focused on the fact that high-rise building development project risks have not reduced in Korea in spite of numerous studies and measures. And it attempted to examine whether existing studies and measures have been presented on the basis of the accurate analysis of existing studies and measures and classify and analyze the characteristics of each phase' s risk factors in the hope that its results would be one reference point as to the measure to prevent high-rise building development project risks in the future. A high-rise building development project is the high risk project as compared with the low-rise project. Because a high-rise development project takes long and is very sensitive to the changing environment. Therefore, in order to succeed the project it becomes necessary to effectively manage the risk involved in the process of the high-rise building development project. The result of this study can be used as the guideline to make the risk management system for the high-rise development project.

• 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 Korean Elementary Science Education
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• v.42 no.4
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• pp.560-576
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• 2023

The purpose of this investigation is to: (1) to derive an improvement factor for inquiry-based simulated teaching-learning in pre-service teacher training programs, and pre-service teachers practice simulated teaching that reflect the improvement factor, (2) to analyze the difference in science intrinsic motivation according to science self-efficacy and inquiry-based simulated teaching-learning experience. To achieve these goals, we recruited five elementary and secondary teachers as experts to help us develop an improvement factor based on expert interviews. Subsequently, third-year pre-service teachers of a university of education participated in our analysis of differences in science intrinsic motivation, according to their level of science self-efficacy and experience with inquiry-based simulated teaching-learning. Our methodology involved applying the analytic hierarchy process to expert interviews to derive improvement factor for inquiry-based simulated teaching-learning, followed by a two-way ANOVA to identify significant differences in science intrinsic motivation between groups with varying levels of science self-efficacy. We also conducted post-analysis through MANOVA statements. The results of our study indicate that inquiry-based simulated teaching-learning can be improved through activities that foster digital literacy, ecological literacy, democratic citizenship, and scientific inquiry skills. Moreover, small group activities and student-centered teaching-learning approaches were found to be effective in developing core competencies and promoting science achievements. Specifically, pre-service teachers prepared a teaching-learning course plan and inquiry-based simulated teaching-learning in seventh-grade in the Earth and Space subject area. Pre-service teachers' science intrinsic motivation analyze significant differences in all levels of science self-efficacy before and after simulated teaching-learning and significant difference in the interaction effect between simulated teaching-learning and scientific self-efficacy. Particularly, group with low scientific self-efficacy, the difference in science intrinsic motivation according to simulated teaching-learning was most significant. Teachers' scientific self-efficacy and intrinsic motivation are needed to improve science achievement and affective domains of students in class. Therefore, this study contributes to suggest inquiry-based simulated teaching-learning reflecting school practices from the pre-service teacher curriculum.