• Journal of Korea Game Society
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• v.9 no.6
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• pp.105-116
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• 2009

The motives of human beings provide reasons to set goals and carry them out. Accordingly, to realize the behaviors of agents similar to human beings, research using motives has been actively conducted. However, it is difficult for this research to cope with unexpected situations in a dynamic environment as does the research in a static environment. Agents can set goals by themselves in the dynamic environment. Furthermore, the goals that are finally selected shall be quickly and definitely set. This study suggests how to determine motives using them in order to enable agents to set goals by themselves. The suggested method compares motives generated by recognizing the environment by phase in real time and identifies the appropriateness of this method. The identified motives are used to set up the goals of agents and to practice the goals. For the appropriateness of the suggested method, the experiment to compare the behaviors of agents with different features in a virtual environment was conducted. The results of the experiment indicate that when several motives are generated, the agents found the most appropriate motive in the present situation. Accordingly, the agents were able to set up optimum goals so that they could cope with dynamic environments using the final motives identified by the determination of motives.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.109-125
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• 1996

Biases and errors in the human being's reasoning process have been studied continuously by the researchers, especially psychlogists and social scientists. These bias phenomenon is classified on the basis of the origin, i. e. motivation and cognition. Furthermore the necessity of research on the bias in the management and management information system areas in increased more and more recently, which have their academic backgrounds in the psychology and social science. The biased information stream is transformed into the systematic error due to the motivation and cognitive bias of human-being, then its resulting phenomena are as follows; 1. the availability of salient information 2. preconceived ideas or theories about peoples and event 3. anchoring and perseverence phenomena. In order to reduce the information errors, Satty suggested the Analytic Hierarchy Process (AHP) that is the subject of this paper and that is widely used for evaluation of complex decision making alternatives. THerefore this paper studies AHP's effects and its limitations in applying to the management area. Thus this paper compared the performances of the 3 models : 1 the traditional additive regression model. 2 regression model using the factor score, and 3 the regression model with AHP. As a result, 3 models produce the different outcomes.

• Korea Trade Review
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• v.46 no.5
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• pp.153-171
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• 2021

The purpose of this study is to compare and derive the importance of motivating factors for start-up when deciding to start a business in the logistics and trading industries, and to compare, derive, and identify which motivating factors have an important influence on the start-up decision. In this study, 4 high-level factors such as career factors, policy/institutional factors, environmental factors, and economic factors and 12 low-level factors were derived from those four factors. Based on this, a questionnaire was distributed to established and prospective entrepreneurs, and the results were analyzed using the AHP (Analytic Hierarchy Process) technique. Manufacturing companies recognized the individual's capabilities and government support for them as important. Beside, service companies recognized the industrial environment that could generate economic benefits as important. Although there are differences in perception by group, it can be seen that factors that are recognized as important within each group have connectivity and show the same directivity.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.513-524
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• 2021

Many organizations have realized that to stand out in today's competitive business world, they need not only academic skills but also emotional intelligence (EI). This study aims to investigate the relationship between EI and the organizational performance of university teachers. The convenient sampling technique has been used to select 200 respondents from 25 universities, and a self-administered research instrument has been employed to collect data from the respondents. The reliability test of items is confirmed by Cronbach's Alpha test using SPSS. Factor analysis has been used to find out the significant constructs of EI, which influence organizational performance. Likert scale and multivariate regression analysis have been used for measuring questionnaire items and testing hypotheses. The key outcomes of this study suggest that interpersonal competence, job performance, effective leadership, motivation and creativity, and social competence have a vital influence on organizational performance. The study also reveals that a decision-making system should be developed and the policymakers and concerned authorities should give more emphasis on key variables of EI that are affecting the advancement of higher education. Further investigation is encouraged to identify the mediating and moderating effects of EI on the relationship between employee work engagement and job performance in the organization.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.379-389
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• 2014

The purpose of this study is to optimize the patient(examinee) flow in a health examination center via a simulation model and to improve operational efficiency. Two experimentation scenarios were implemented into the simulation model to determine which proposed scenario provides better improvement in terms of the following performance measures: LOS(Length of Stay), staff utilization, and occupancy level. The simulation results demonstrated that there was no significant difference in response results of two scenarios. Although the original motivation of this study was suggest optimal policy for a patient(examinee) flow, the insight into applying simulation in efficiently managing hospital operations is of more value. Simulation approach is a powerful technique that supports efficient decision-making compared to traditional healthcare management approach based on past experience, feelings, and intuition. Therefore, the proposed experimentation model has wide applicability in healthcare systems.