• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.357-369
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• 2009

With the development and generalization of internet and information technology, airlines has tried to reduce their business expenses and commissions to travel agencies and enhance service qualities through service automation and simplification, such as internet booking and ticketing, self check-in, in-flight internet and RFID for checked baggage. The statistical techniques conducted for this empirical analysis are frequency analysis, reliability analysis, factor analysis, confirmatory factor analysis and multiple regression analysis. This research has tried to examine factors of airline e-services that influence on recommendation re-purchase intention and satisfaction. Results has found that only on-line reservation and ticketing factor had significant effect for recommendation and re-purchase intention and all e-service factors produced significant effect to total satisfaction. It was also recommend that airlines have to provide easy and more familiar e-service system to their passengers to deliver better services.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.276-287
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• 2021

Project Management Information System (PMIS) is a special purpose information system that is created to provide useful information for project managers and participants to make effective and efficient decision making during projects. The use of PMIS is increasing in project based industries such as construction, defense, manufacturing, software development, telecommunication, etc. It is generally known that PMIS helps to improve the quality of decision making in project management, and consequently improves the project management performance. However, it is unclear what are the difference of PMIS impacts between industries, and still need to be studied further. The purpose of this study is to compare the impact of PMIS on project management performance between industries. We assume that the effects of PMIS will be different depending on the industry types. Five hypotheses are established and tested by using statistical methods. Data were collected by using a survey questionnaire from those people who had experience of using PMIS in various project related industries such as construction, defense, manufacturing, software development and telecommunication. The survey questionnaire consists of 5 point scale items and were distributed through e-mails and google drive network. A total of 181 responses were collected, and 137 were used for analysis after excluding those responses with missing items. Statistical techniques such as factor analysis and multiple regression are used to analyze the data. Summarizing the results, it is found that the impacts of PMIS quality on the PM performance are different depending on the industry types where PMIS is used. System quality seems to be more important for improving the PM performance in construction industry while information quality seems more important for manufacturing industry. As for the ICT and R&D industries, PMIS seems to have relatively lesser impact compared to construction and manufacturing industries.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.153-167
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• 2019

Purpose: The aim of this study was to examine factors influencing health-related quality of life in middle and old adult one-person households. Method: This study carried out secondary analysis using the data from the $7^{th}$ Korean National Health and Nutrition Examination Survey. Subject samples who were selected are 497 middle and old adult one-person households over 40 years. Data were analyzed using descriptive statistics, simple and multiple regression techniques with the SPSS/WIN 24 program. Result: Factors influencing health-related quality of life in middle adult one-person households were activity limitation, depression, exercise, smoking habits with 57% prediction. In male old adult one-person households they were subjective health, metabolic syndrome, activity limitation, perceived stress with 44.8% prediction and in female old adult one-person households they were subjective health, activity limitation, home income with 35.9% prediction. Conclusion: Therefore, to improve their health-related quality of life it needs to develop & to apply national and local promotion policy and intervention program on health-related quality of life of middle and old adult one-person households.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.81-91
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• 2019

The ROK military uses Performance Based Logistics(PBL) as one of the ways to utilize civilian resources and advanced techniques. However, the Korean PBL is mainly focused on purchasing and repairing parts, which is not contributing to the improvement of the availability of overall system. The objective of this study is to suggest the methodology to evaluate the PBL metrics using the simulation and meta model. A meta model is a regression model to analyze the effect of the PBL through simulating various scenarios with performance metrics. As a result, if the PBL is limited to the part level, the performance has little influence on the availability of overall system. In addition, analysis using the meta model shows that it cannot achieve the performance targets when the same metrics are applied to various items without considering the characteristics of the applied items. Therefore, in order to improve availability, PBL coverage should be extended to a system level that includes key components that have a large impact on availability. If multiple items are included in the PBL coverage, the metrics should be applied differently, taking into account the characteristics of each item.

• The Korean Journal of Psychodrama
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• v.21 no.2
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• pp.41-56
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• 2018

The purpose of this study is to investigate the effects of ego-resilience and job stress on job satisfaction of the rehabilitation teachers of residential facility for the disabilities. For this purpose, 193 questionnaires were collected and used for final analysis. Statistical techniques such as descriptive statistics, correlation analysis, and multiple regression analysis were used in the SPSS 23.0 statistical program. The results of the analysis are as follows; First, Levels of perception of ego-resilience and job satisfaction were higher than medium level, and job stress was lower than medium level. Second, Job satisfaction of the subjects were higher as the interpersonal relationship, were lower as the stress of personal role and daily work, relationship with facility and supervisor, and client relationship. These results suggest that ego-resilience and job stress are closely related to job satisfaction and that it is effective to increase ego-resilience and reduce job stress as a way to increase job satisfaction of rehabilitation teachers. Since rehabilitation teachers provide human services, they need support from peer counseling, education, and programs because their emotional exhaustion appears in interpersonal relations.

• Journal of Distribution Science
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• v.20 no.8
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• pp.93-103
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• 2022

Purpose: Online shopping festivals have quickly become the newest trend in online shopping worldwide due to the COVID-19 pandemic. This has led to marketing distribution channels that traditionally emphasized traditional techniques having turned to electronic commerce platforms. Although the pandemic scenario encourages online purchasing, other factors, such as the influence of participation intention to shop during the Online Shopping Festival, must also be considered. Research design, data and methodology: Multiple linear regression analysis was used to test the hypothesis based on data from 121 respondents who are actively involved with online shopping activities in Klang Valley, Selangor. Results: The results of this study show that promotion categories and the perceived influence of mass participation have a significant influence on participation intention. Meanwhile, the perceived temptation of price promotion and perceived fun promotional activities did not significantly influence participation intention. Conclusions: Theoretically, this study contributes to the literature by using the Theory of Planned Behavior and Stimulus-Response models to explain the factors that drive participation intention for online shopping. In practice, this study attracts and encourages customers to shop during the festival day because various attractive promotions are offered by sellers in Malaysia.

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• The Journal of Bigdata
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• v.6 no.2
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• pp.85-98
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• 2021

The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.