• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.1
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• pp.143-165
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• 2017

This study aims to examine the effect of students' library after-school activities on the students and the school library from a marketing perspective and to recommend marketing strategies for school libraries. For the purpose, a questionnaire survey was conducted at nine middle schools in Seoul and Gyeonggi Province, and a series of statistical analyses with the survey data was performed in order for hypothesis testing. The results suggest that the effects of the school library marketing including customer loyalty were influenced by the student assistants' activities such as internal and experiential marketing factors. Furthermore, the activities of student library assistants can be linked to word-of-mouth marketing. Based on the results, ways to use student assistants in school library marketing are suggested.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.558-565
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• 2015

The current methods, such as Binomial Probability Distribution or T-test, to calculate the number of samples of Precise Guided Missiles(PGMs) for test and evaluation are statistically problematic to make reasonable decisions thus use the budget efficiently. Also, the method to verify an accuracy rate of the PGMs using these old methods seems to be lack of objectiveness. This paper presents an effective, efficient solution adapted from the clinical medicine testing method of healthcare industry research, which may overcome our problems. This method considers and addresses both manufacturers' and consumers' risk simultaneously. By taking into account the weapon system project characteristics which take a relatively longer time and require higher-level budget, this approach also suggests a reasonable and feasible method to determine the amount of samples of the PGMs and making a criterion to verify an accuracy rate of the missiles that are required to test. Thus, this method is expected to contribute to acquisition and operations of high-reliability PGMs by MND and its field-users.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.21-31
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• 2007

This study is aimed at the development of a runoff forecasting model by using the Fuzzy inference system and Neural Network model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting. The Neuro-Fuzzy (NF) model were used in this study. The NF model, recently received a great deal of attention, improve the existing Neural Networks by the aid of the Fuzzy theory applied to each node. The study area is the downstreams of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model respectively. The schematic diagram method and the statistical analysis are conducted to evaluate the feasibility of rainfall-runoff modeling. The model accuracy was rapidly decreased as the forecasting time became longer. The NF model can give accurate runoff forecasts up to 4 hours ahead in standard above the Determination coefficient $(R^2)$ 0.7. In the comparison of the runoff forecasting using the NF and TANK models, characteristics of peak runoff in the TANK model was higher than ones in the NF models, but peak values of hydrograph in the NF models were similar.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.191-199
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• 2020

This study aims to investigate what selection attributes of customer value are more crucial when consumers choose the dessert at convenience store, which is the fastest growing retail format in Korea recently. Factors considered to be important when customers buy dessert, such as economics, diversity, convenience, and safety were examined and also relationship between customer value and satisfaction was analyzed. The survey was conducted through online (including SNS) from April 6, 2019 to April 30, 2019. A total of 303 questionnaires were used for empirical analysis. Factor analysis was performed to verify the validity and reliability of the measured variables. And the structural equation model (SEM) was used as a statistical method for hypothesis testing. As a result, factors as 'diversity', 'safety' were revealed to significantly affect hedonic customer value, while 'convenience' was shown to affect utilitarian customer value significantly. In addition, the path that customer value leads to satisfaction was also found to be significant. The result of the study suggests that selection attributes of convenience store dessert and providing meaningful implications of related hedonic and utilitarian values when customers purchase dessert at convenience store. This study provided practical implications for managers of the fiercely competitive dessert industry.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.53-62
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• 2020

The study aims to provide some plausible explanation for why Vietnamese listed companies only stop at the level of truthful presentation of information related to accounting data through the opinion of independent auditors. The information is only at the level of compliance with the requirements of Circular 155/2015/TT-BTC in form, but in essence is sketchy. What factors affect the level of voluntary disclosure of listed companies in Vietnam? In order to identify the factors affecting voluntary information disclosure on annual reports of listed companies, the study collected data on annual reports of 122 companies listed on the stock market in Ho Chi Minh City in the period 2015-2018 and uses regression analysis methods. The research presents 8 factors affecting the level of voluntary information disclosure including: Firm size, Listed time, Profitability, Solvency, Separation of board of directors and executive director, Board size, Organizational ownership and Foreign ownership. Next, the study conducted descriptive statistical analysis correlation coefficient analysis to examine the correlation and relevance of independent variables measured by the scale ratio, testing multiple linear regression model. The results of the study show that factors listed time, profitability and organizational ownership affecting voluntary information disclosure on annual reports of listed companies in Vietnam.

• Journal of Applied Reliability
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• v.15 no.1
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• pp.33-43
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• 2015

The importance of reliability engineering has increased in recent years. Many studies have been carried out into reliability in a variety of research domains, largely focused on industrial engineering, mechanical engineering, electronics engineering, materials engineering and statistical science. However, most of these studies were carried out without collaboration between different areas of study. Here, we analyze research papers and patents regarding reliability that were published during the past five years, and describe core trends in the development of reliability technology from a multidisciplinary perspective.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Fisheries and Aquatic Sciences
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• v.14 no.3
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• pp.226-229
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• 2011

This study was conducted to develop a proficiency test (PT) specimen for the enumeration of Escherichia coli, a sanitary indicative bacterium, in molluscan bivalve shellfish. The common mussel Mytilus edulis was chosen as a matrix for the PT specimen. Mussels were artificially bioaccumulated for approximately 3 h with E. coli culture. After determining the homogeneity of samples, samples were distributed to 17 participants involved in the proficiency testing program. The enumeration of E. coli was performed by the most probable number method of the American Public Health Association. Statistical evaluations of the results obtained from inter- and intra-laboratory variation indicated no significant differences in the accuracy of these techniques between participants, indicating z-scores of ${\leq}{\pm}2$ and suggesting that preparation of the PT specimen for enumerating E coli in shellfish was successful.

• Knowledge Management Research
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• v.10 no.3
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• pp.1-16
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• 2009

Recent trends in business are characterized by recognition of the importance of knowledge assets. Especially, this phenomenon is receiving more momentum as our society goes digital rapidly. The concept of Knowledge Management has long been used as an effective way of restructuring the management process of a firm. In literature regarding knowledge management, a number of successful cases were reported, where knowledge-based management activities could dramatically enhance a firm's strategic performance for years. However, there exists very little research investigating the effect of knowledge management on corporate competitiveness. Researchers as well as practitioners need more rigorous results about such issues, especially how much the knowledge management strategic initiatives may have an influence on the firm's competitiveness. In this sense, we need an empirical study adopting statistical approaches due to the existence of abundant knowledge management case studies. To fill this research void, this study proposes a research model testing whether knowledge management activities could improve corporate competitiveness. We collected 321 valid questionnaires from participants in knowledge management activities in the tourism industry and applied a structural equation model. Empirical results showed that knowledge management activities have a positive impact on knowledge management efficiency and finally corporate competitiveness. We hope that this study will shed light on further rigorous studies considering tourism industry as a major strategic activity.