• Journal of the Korean association of regional geographers
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• v.14 no.2
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• pp.126-140
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• 2008

The purpose of this study was to compare the motivation factors between two groups: Chinese and Japanese tourists in Jeju-do. Also, this study offered an integrated approach to attempts to extend the theoretical and empirical research evidence on causal relationships of each group among motivation factors, perceived value, satisfaction, and intention to revisit and finally, the result of path analysis was compared. The results indicated that Chinese tourists' perceived value was positively affected by tourists' convenience(i.e. reservation system and tour guide) and their cultural experience had an influence on tourists' satisfaction significantly. On the other hand, Japanese tourists' satisfaction was positively affected by tourists' convenience and their perceived value significantly influenced on tourists' intention to revisit. The findings of this study can be used as a marketing strategy for each tourist group.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.589-595
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• 2009

This paper proposes an object-oriented software development guidance and an evaluation index for the productivity related to Spring Framework 2.5. Spring Framework is a known successful open source standard model for lightweight container architecture. Non EJB and the EBJ architecture to resolve the problem with benefits to support the new architecture is a lightweight container architecture. This architecture, such as the EJB, but not heavy, to provide all of the architecture is possible. The lightweight container architecture is most often used in business spring framework is well-known architecture. Therefore, this research has the Non EJB and the EJB to solve the advantages and disadvantages developed to support the latest spring framework 2.5 lightweight container architecture based on the design and implementation of a hotel reservation system with the objective through the specification of the software previously to provide guidance to development productivity.

• Journal of Korean Medical Science
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• v.33 no.53
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• pp.341.1-341.11
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• 2018

Background: The converging epidemics of tuberculosis (TB) and end-stage renal disease (ESRD) have generated a significant public health burden, however, previous studies have been limited to a small number of patients. This nationwide cohort study aimed to assess the rate of developing active TB among patients receiving dialysis for ESRD. Methods: The Korean national health insurance database was used to identify patients receiving dialysis for new-onset ESRD during 2004-2013, who were propensity score matched to an equivalent number of non-dialysis subjects from the general population. The incidences of active TB in the ESRD and control cohorts were calculated for 2004-2013, and multivariable Cox proportional hazards model was used to evaluate the ESRD-related risk of active TB. Results: During 2004-2013, 59,584 patients received dialysis for newly diagnosed ESRD. In the dialysis and control cohorts, 457 (0.8%) and 125 (0.2%) cases of active TB were detected, respectively. Patients with ESRD were associated with a significantly higher risk of active TB compared to the controls (incidence rate ratio, 4.80). The ESRD cohort had an independently elevated risk of active TB (adjusted hazard ratio, 4.39; 95% confidence interval, 3.60-5.37). Conclusion: We found that patients receiving dialysis for ESRD had an elevated risk of active TB. These results highlight the need for detailed and well-organised guidelines for active TB screening among patients with ESRD.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.4
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• pp.685-702
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• 2021

This study aims to discover how the perceived attributes of luxury fashion platforms affect consumer trust and satisfaction as well as online word-of-mouth intention. Based on a literature review, this study derived four dimensions of perceived attributes: brand assortment size, exclusivity, convenience, and personalization. The paper presents findings from an online survey targeting 359 consumers in their 20s to 30s who had recent experience with luxury fashion platforms. Based on the collected data, a structural model equation analysis was performed using AMOS 22.0 and SPSS 26.0. The findings illustrated that brand assortment size, exclusivity, and personalization had positive effects on consumers' platform trust. In addition, brand assortment size and convenience had a positive impact on satisfaction. Overall, the findings of the study illustrate that perceived attributes of luxury fashion platforms have a significant impact on consumers' platform trust and satisfaction and online word-of-mouth intentions. This study reveals that consumers' trend orientation moderates the effects of consumer attitude and behavioral intention. The academic practice of this study has laid the foundation for understanding mechanisms of marketing strategies by providing the characteristics of platforms in the luxury fashion industry.

• Journal of Preventive Medicine and Public Health
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• v.52 no.3
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• pp.161-169
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• 2019

Objectives: The objective of the present study was to validate a shortened transformational leadership (TL) scale (12 items) comprising core TL behaviour and to test the associations of this shortened TL scale with depressive symptoms. Methods: The study used cross-sectional data from 1632 employees of the overall workforce of a middle-sized German company (51.6% men; mean age, 41.35 years; standard deviation, 9.4 years). TL was assessed with the German version of the Transformational Leadership Inventory and depressive symptoms with the Hospital Anxiety and Depression Scale (HADS). The structural validity of the core TL scale was assessed with confirmatory factor analysis. Associations with depressive symptoms were estimated with structural equation modelling and adjusted logistic regression. Results: Confirmatory factor analysis and structural equation modelling showed better model fit for the core TL than for the full TL score. Logistic regression revealed 3.61-fold (95% confidence interval [CI], 2.20 to 5.93: women) to 4.46-fold (95% CI, 2.86 to 6.95: men) increased odds of reporting depressive symptoms (HADS score >8) for those in the lowest tertile of reported core TL. Conclusions: The shortened core TL seems to be a valid instrument for research and training purposes in the context of TL and depressive symptoms in employees. Of particular note, men reporting poor TL were more likely to report depressive symptoms.

• International Area Studies Review
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• v.22 no.3
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• pp.201-219
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• 2018

This paper suggested a theoretical model, in which a security-based(secured loan, non-secured loan) credit agreement determines the form of corporate cost function through a loaning company's cost minimization in the light of a company which behaves monopolistically in product markets. Also, this paper analyzed the influence of a corporate credit agreement on market equilibrium, and economic welfare in product markets. As a result, it was found that in case a company, whose equity capital is small, implements borrowing based on a secured loan from a financial institution, the company comes to face borrowing restraints, in which the company has no choice but to get a loan within the scope of securities. When a company offers its capital goods, i.e. a production factor, as a security, there occurs a distortion to the production factor input ratio. Meanwhile, when a company comes to get a loan based on an unsecured loan, for which the interest rate is high, marginal cost rises; accordingly, the company comes to choose a credit agreement aiming at maximizing its profits. However, a company's choice of a credit agreement is not quite desirable from a consumer's viewpoint, and from the whole economic point of view; overall, such a choice is likely to aggravate economic welfare.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.329-333
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• 2021

V2G (Vehicle to Grid) technology for an EV (Electric Vehicle) has been assumed as so promising in a near future for its useful energy resource concept but still yet to be developed around the world for specific service purposes through various R&BD projects. Basically, V2G returns power stored in vehicle at a cheaper or unused time to the grid at more expensive or highly peaked time, and is accordingly supposed to provide such roles like peak shaving or load levelling according to customer load curve, frequency regulation or ancillary reserves, and balancing power fluctuation to grid from the weather-sensitive renewable sources like wind or solar generations. However, it has recently been debated over its prominent usage as diffusing EVs and the required charging/discharging infrastructure, partially for its addition of EV ownership costs with more frequent charging/discharging events and user inconvenience with a relative long-time participation in the previously engaged V2G program. This study suggests that a Korean DR (Demand Response) service integrated V2G system especially based upon a dynamic charge/pause/discharge scheme newly proposed to ISO/IEC 15118 rev. 2 can deal with these concerns with more profitable business model, while fully making up for the additional component (ex. battery) and service costs. It also indicates that the optimum economic, environmental, and grid impacts can be simulated for this V2G-DR service particularly designed for EV aggregators (V2G service providers) by proposing a specific V2G engagement program for the mediated DR service providers and the distributed EV owners.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.