• Journal of Korean Medicine Rehabilitation
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• v.32 no.1
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• pp.89-106
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• 2022

Objectives In this study, a scoping review was conducted to inform decision-making related to traditional Korean medicine for people with disabilities in the future. Methods Seven databases were searched to find previous studies on traditional Korean medicine for people with disabilities. Studies published until August 2021 were considered. Using the methodology of scoping review, research on traditional Korean medicine for people with disabilities was reviewed with the following steps: 1) drawing research questions, 2) searching for related studies, 3) selecting studies, 4) extracting data, and 5) analyzing and reporting results. Results Out of 2,072 studies, 7 research papers and 10 reports were finally selected. The research papers included 5 cases studies, 1 survey study, and 1 chart review. Most studies used herbal medicine and acupuncture treatment, but the reports on the interventions were not detailed. The reports included policy studies, project performance guidelines, and project results reports, and most of the evaluation indicators tended to be standardized. Conclusions This study reviewed the literature on traditional Korean medicine for people with disabilities. It presents future directions for clinical research on traditional Korean medicine for people with disabilities and can be used to inform healthcare policies and clinical practice. In the future, quantitative research such as clinical trials, meta-analysis, and health insurance big data analysis is needed to understand the current status and effects of traditional Korean medicine for people with disabilities. In addition, qualitative research is necessary to identify unmet demands of traditional Korean medicine for people with disabilities.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Korean journal of communication and information
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• v.34
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• pp.244-274
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• 2006

To explore the social construction of the concept of leadership, image of media mogul depicted in the popular business newspaper, the Wall Street Journal, was analyzed. Then, the reconstructed image of the CEO was compared with the firm's stock price change to see their relationship, if any. This paper focused on the case of Steve Case (previous chairman of AOL Time Warner), who was the leader of the world largest media company. The period for the analysis was three years and five months from his inauguration(January 2000) to the resignation(May 2003). In general, CEO of a firm represents the firm itself. Thus, the image of the CEO is highly transcends to the image of the firm as well. Consequently, the image of CEO might have an impact on the firm's performance. Since business newspaper works as one of the most important information intermediaries in the stock market, the image of CEO constructed in the newspaper might be a critical indicator for the investors. The results revealed that media coverage of Steve Case was commensurate with the financial performance, particularly stock price change of the AOL Time Warner.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.123-134
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• 2021

This study was conducted for the purpose of convergence exploration of evidence-based practices for the advancement of performance analysis of the extracurricular activities at Duksung Women's University. The research method consisted of an expert meeting procedure along with a procedure for analyzing previous studies dealing with the performance analysis of the university's extracurricular activities in the field of pedagogy. The contents of this study consisted of presenting some facts that should be based on evidence for the advancement of performance analysis of the extracurricular activities after the establishment of the center for extracurricular activities in Duksung Women's University. And in practices, the development and diagnostic analysis of tools for diagnosing extracurricular customized learning capabilities, data-based multidimensional analysis (IR system), continuous monitoring analysis through extracurricular certification, and analysis based on feedback tools were presented in a convergence perspective and context. As a result of the study, the evidence-based practices for the advancement of the performance analysis of the extracurricular activities at Duksung Women's University guarantees the validity and stability of the performance evaluation and feedback system of the extracurricular activities at Duksung Women's University, and has a close influence on the extracurricular education work of other universities analyzed as possible.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.83-92
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• 2021

Recently many organizations have adopted 'untact' work due to social distancing caused by Coronavirus-19. To clarify some controversy about the effectiveness from 'untact' work, it is necessary to examine the cognition of organizational members. This study identified issues in 'untact' work from the literature review, analyzed the content of in-depth interviews with workers and managers experiencing 'untact' work, and compared both groups' cognition by applying the co-orientation model. Both groups pointed out the communication difficulty as the top disadvantage and showed no significant differences in job satisfaction, organizational commitment, and work-life balance. However, the two groups showed significant differences in their cognition about performance evaluation (agreement and workers' congruence) and productivity enhancement (workers' accuracy). This paper has an academic contribution in that it has focused on cognitive gaps between workers and managers, urges organizations to devise ways to reduce the gaps, and suggests future studies with quantitative approaches.

• Animal Bioscience
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• v.35 no.12
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• pp.1817-1826
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• 2022

Public concern on the methods of raising food-producing animals has increased, especially in the last two decades, leading to voluntary and mandated changes in the animal production methods. The primary objective of these changes is to improve the welfare of farm animals. The use of gestational stalls is currently a major welfare issue in swine production. Several studies assessed the welfare of alternative housing systems for gestating sows. A comparative study was performed with gestating sows housed in either individual stalls or in groups in a pen with an electronic sow feeder. This review assessed the welfare of each housing system using physiological, behavioral, and reproductive performance criteria. The current review identified clear advantages and disadvantages of each housing system. Individual stall housing allowed each sow to be given an individually tailored diet without competition, but the sows had behavioral restrictions and showed stereotypical behaviors (e.g., bar biting, nosing, palate grinding, etc.). Group-housed sows had increased opportunities to display such behavior (e.g., ability to move around and social interactions); however, a higher prevalence of aggressive behavior, especially first mixing in static group type, caused a negative impact on longevity (more body lesions, scratch and bite injuries, and lameness, especially in subordinate sows). Conclusively, a more segmented and diversified welfare assessment could be beneficial for a precise evaluation of each housing system for sows. Further efforts should be made to reduce aggression-driven injuries and design housing systems (feeding regimen, floor, bedding, etc.) to improve the welfare of group-housed sows.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.193-215
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• 2016

In recent years, the proliferation of diverse social networking services has led users to use many mediums simultaneously depending on their individual purpose and taste. Besides, while collecting information about particular themes, they usually employ various mediums such as social networking services, Internet news, and blogs. However, in terms of management, each document circulated through diverse mediums is placed in different categories on the basis of each source's policy and standards, hindering any attempt to conduct research on a specific category across different kinds of sources. For example, documents containing content on "Application for a foreign travel" can be classified into "Information Technology," "Travel," or "Life and Culture" according to the peculiar standard of each source. Likewise, with different viewpoints of definition and levels of specification for each source, similar categories can be named and structured differently in accordance with each source. To overcome these limitations, this study proposes a plan for conducting category mapping between different sources with various mediums while maintaining the existing category system of the medium as it is. Specifically, by re-classifying individual documents from the viewpoint of diverse sources and storing the result of such a classification as extra attributes, this study proposes a logical layer by which users can search for a specific document from multiple heterogeneous sources with different category names as if they belong to the same source. Besides, by collecting 6,000 articles of news from two Internet news portals, experiments were conducted to compare accuracy among sources, supervised learning and semi-supervised learning, and homogeneous and heterogeneous learning data. It is particularly interesting that in some categories, classifying accuracy of semi-supervised learning using heterogeneous learning data proved to be higher than that of supervised learning and semi-supervised learning, which used homogeneous learning data. This study has the following significances. First, it proposes a logical plan for establishing a system to integrate and manage all the heterogeneous mediums in different classifying systems while maintaining the existing physical classifying system as it is. This study's results particularly exhibit very different classifying accuracies in accordance with the heterogeneity of learning data; this is expected to spur further studies for enhancing the performance of the proposed methodology through the analysis of characteristics by category. In addition, with an increasing demand for search, collection, and analysis of documents from diverse mediums, the scope of the Internet search is not restricted to one medium. However, since each medium has a different categorical structure and name, it is actually very difficult to search for a specific category insofar as encompassing heterogeneous mediums. The proposed methodology is also significant for presenting a plan that enquires into all the documents regarding the standards of the relevant sites' categorical classification when the users select the desired site, while maintaining the existing site's characteristics and structure as it is. This study's proposed methodology needs to be further complemented in the following aspects. First, though only an indirect comparison and evaluation was made on the performance of this proposed methodology, future studies would need to conduct more direct tests on its accuracy. That is, after re-classifying documents of the object source on the basis of the categorical system of the existing source, the extent to which the classification was accurate needs to be verified through evaluation by actual users. In addition, the accuracy in classification needs to be increased by making the methodology more sophisticated. Furthermore, an understanding is required that the characteristics of some categories that showed a rather higher classifying accuracy of heterogeneous semi-supervised learning than that of supervised learning might assist in obtaining heterogeneous documents from diverse mediums and seeking plans that enhance the accuracy of document classification through its usage.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.