Probability plotting position is generally used for the graphical analysis of the annual maximum quantile and the estimation of exceedance probability to display the fitness between sample and an appropriate probability distribution. In addition, it is used to apply a specific goodness of fit test. Plotting position formula to define the probability plotting position has been studied in many researches. Especially, the GEV distribution which is an important probability distribution to analyze the frequency of hydrologic data was popular. In this study, the theoretical reduced variates are derived using the mean value of order statistics to derived an appropriate plotting position formula for the GEV distribution. In addition, various forms of plotting position formula considering various sample sizes and coefficients of skewness related with shape parameters are applied. The parameters of plotting position formulas are estimated using the genetic algorithm. The accuracy of derived plotting position formula is estimated by the errors between the theoretical reduced variates and those by various plotting position formulas including the derived ones in this study. As a result, the errors by derived plotting position formula is the smallest at the range of shape parameter with -0.25~0.10.
The paper is to estimate willingness-to-pay (WTP) for residential water quality improvement in Busan, using non-parametric approach. There are several significant advantages of non-parametric approach, compared to parametric methods. That is, no probability distribution assumption is necessary so that there are no needs to assume or test goodness of fit, model specification and heteroscedasticity statistically. For the reliability and the validity of contingent valuation method a survey was conducted for 665 respondents, who were sampled by stratified random sampling method, by personal interview method. The result of mean WTP for residential water quality improvement in Busan was estimated to be 3,190 won to 3,331 won per month per household, while median WTP being 1,750 won. Provided that our sample is broadly representative of the Busan's population, an estimate of the annual aggregated benefit of residential water improvement for all Busan households is approximately 50.2 billion won in case of mean WTP or 27.5 billion won in case of median WTP.
This study has three primary purposes, firstly to identify how leader's personal characters influence to his/her transformational leadership, secondly to find how transformational leadership influences to innovative behavior, finally to explore how organizational cultures moderate between transformational leadership and innovative behavior. The first part of the study, based on literature study on transformational leadership, provides insight into what are antecedents, moderators and dependent variable in transformational leadership. Firstly, leader's personal characters are selected as antecedent variables such as extroversion and self-efficacy. Secondly, innovative behavior is introduced as a dependent variable. Thirdly, two types of organizational culture are considered as moderators between leader's personal character and leadership In this study, a comprehensive research model and hypothesis were empirically tested based on data from three types of questionnaires involving 663 employees in Korean organizations. In order to test the hypotheses, we have used Structural Equations Model (SEM) from AMOS7.0. In this analysis, we have employed raw data as it is instead of correlation matrix or covariance matrix. We have tested hypotheses by examining the significance of each path of the model, and gone through the process of testing the goodness of fit of the model itself. The results of statistical analysis show the following. Firstly, one of leader's personalities, self-efficacy has positive effect on his/her transformational leadership, but extroversion does not have positive effect. Secondly, transformational leadership has positive effect on innovative behavior. Finally, there was not any cultural moderating effects between transformational leadership and innovative behavior.
The purpose of the study is to find factors to measure Museum Service Quality (SERVQUAL) and Visitors' motivation. The survey was analyzed with 214 questionnaires out of 230 ones and it was conducted by those who have once visited the National Museum of Korea. Pre-test was conducted from May, 2013. Deriving adequate Service Quality Measures in Museum, factor analysis was applied. From 29th of July 2013 to 23rd of August 2013, surveys were conducted through online and off-line. Using the SPSS 20.0 statistics, Population analysis was applied frequently. In order to ensure the reliability, cronbach ${\alpha}$ value was evaluated. Fit of the research model and the suitability of the path coefficients were evaluated through AMOS to ensure the validity and reliability. As a result, in terms of Analysis on Service Quality, it influenced significantly on the degree of satisfaction. Visitors' motivation also had partial influence on the degree of satisfaction. Moreover, Behavioral Intentions were also influenced by the degree of satisfaction.
This study is to develop a fatigue scale for the private security guard for a private security guard to effectively manage fatigue were tested for validity and reliability. To achieve that, this study employed purposive sampling to draw 300 samples among private security guards working for the security firms registered in Seoul District Police Office as of 2014, and used 259 samples for final analysis. Fatigue scale was developed on the basis of the scale development methods and procedures in previous studies. For data processing, SPSSWIN 18.0 program was applied to conduct exploratory factor analysis, reliability analysis, and correlation analysis, and AMOS 18.0 program was employed to conduct confirmatory factory analysis. This study came to the following conclusion. First, the validity of the fatigue measurement tool was high. In other words, content validity was high, and construct validity was also high. The factor loading of exploratory factor analysis was .452, and the standard value of confirmatory factor analysis was .403. Therefore, the tool satisfied the basis value of fit index. The sub categories of fatigue, finally drawn from validity test, were mental fatigue, nervous system dysfunction, physical deformity, and exhaustion. Secondly, the reliability of the fatigue measurement tool was high. In other words, according to item analysis, the correlation between items and their sub categories, and between items and total score was more than .602, relatively high. And the reliability coefficient (Cronbach's ${\alpha}$) was high, or more than .658. Fatigue Scale tool will be developed by private security guards in the present study is to measure the validity and reliability verification is clearly tired of the private security guards than the tools that are used in other studies in the existing area.
This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.
Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.
Journal of the Korea Academia-Industrial cooperation Society
/
v.18
no.6
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pp.197-208
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2017
The purpose of this study was to construct and test a structural equation model for college student's gambling behavior. A structured questionnaire was completed by 246 college students to analyze the relationships between perspective factors(irrational gambling belief), psychological factors(depression, anxiety), social factors(parental monitoring, parental support, friend support), self-control and gambling behavior. The moderating effects of gender, friends and family's gambling behaviors were examined. The data were analyzed using SPSS 21,0 and AMOS 20.0 programs. Self-control and psychological factors directly affected the college student's gambling behavior, while perspective factors and social factors affected it indirectly. The model fit indices of the modified model were suitable for the recommended levels. The overall study findings suggest the need to develop a gambling prevention program for college students that reinforces self-control, parental monitoring and support, and friend support while reducing irrational gambling belief, depression, and anxiety. An approach that considers gender and a development of a group counseling program for family or friends are also required.
Purpose - This research examined the effect of hedonic and utilitarian eating-out motivations on the evaluation of restaurant selection attributes in a fashion premium outlet. Additionally, the influence of experimental and functional attributes on customers' preference for hedonic eating-out motives and utilitarian eating-out motives, and variation of moderating effects through the gender was examined. Research design, data, and methodology - A survey was conducted to verify the established research hypothesis. The questionnaire items for the research were modified to fit the situation of the present study. In order to elaborate the questionnaire, the literature of the previous researchers was reviewed and supplemented. The survey conducted 207 online questionnaires for consumers who have visited domestic fashion premium outlets from July 4, 2017 to July 27, 2017. A total of 207 questionnaires were collected, and a total of 206 questionnaires were used for the empirical analysis after excluding one inappropriate response. In order to verify the reliability and validity of the measured variables, exploratory factor analysis and reliability analysis were performed using SPSS 20.0. Next, the structural equation model (SEM) statistical method was used to test the hypotheses of the study. Results - Hedonic motivation had more influence on experimental attribute importance than the functional attribute importance of the restaurant. However, this result was different depending on the gender. The effects of hedonic motives on empirical attributes were more influenced by female groups, and when influencing functional attributes, male groups were more influenced. However, it was statistically significant (p <0.05) in the female group only when the hedonic eating out motives influenced empirical attributes. Conclusions - This study analyzed the effect of eating-out motivation on the restaurant preference attributes and suggested practical implications. First, customers with hedonic eating-out motivations were evaluating experiential attributes to be more important than functional attributes. Second, for customers who are motivated to use practical eating-out habits, companies should provide services that meet practical and economic needs. In particular, female customers visiting restaurants need differentiated marketing strategies that make them feel new experiences rather than practical ones. In addition, it is necessary to study more complex and integrated studies which will influence restaurant selection attributes of premium outlets customers by adding various eating out motives and selection attributes.
Journal of the Korea Academia-Industrial cooperation Society
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v.19
no.2
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pp.173-184
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2018
This study was conducted to construct and test structural equation modeling of the causal relationship of job satisfaction of nursing staff with satisfaction, revisit intention and recommendation of the hospital by patients. The data utilized in this study were the second data acquired from the 2012 Public Hospital Evaluation Programme. The subjects of this study were 2,375 nursing staff and 3,433 patients at 39 district public hospitals. The instrument of job satisfaction of nursing staff consisted of five factors and 13 items. The instruments of satisfaction, revisit intention, and recommendation to others of patients consisted of one question on an 11 point scale (0: very negative, 10: very positive). The data were analyzed using SPSS version 20.0 and AMOS version 20.0. Model fit indices for the hypothetical model were suitable for the recommended level: model of in-patient ${\chi}^2$ 904.598 (df=81, p<0.001), GFI 0.938, AGFI 0.900, RMSR 0.076, mode of out-patient ${\chi}^2$ 869.021(df=81, p<0.001), GFI 0.940, AGFI 0.900, RMSR 0.074. In conclusion, nursing staff are the largest group in public hospitals, and they provide direct care to patients. Therefore, job satisfaction of nursing staff should be enhanced to improve satisfaction of patients because their attitude significantly influences patient satisfaction.
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