• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.43-54
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• 2024

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.169-174
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• 2024

This study purports to identify the factors that contribute to the classification of age groups or generations of Koreans. Independent variables such as respondents' attitudes toward welfare, attitudes toward equity, education level, perception of inequality in Korean society, tax awareness, and health status are included in the model that were put into the analysis with the main interest. Since this study does not construct any hypothesis prior to analysis, the nature of this study can be said exploratory. The data utilized for the analysis are from the 17th year of the Korean Welfare Panel collected in 2022, and a linear discrimination analysis technique will be used. First and foremost, a theoretical review of the generational classification will be conducted through domestic and international literature in the past. To date, there is no quantitative studies in Korea that have a significant influence on the generational classification. Therefore, in this study, a theoretical review of political tendencies and values, which are estimated to have a significant influence on the generational classification, that is, the difference between generations, will be significant. The perception and attitude toward welfare will be discussed in the review of values. Next, analysis models, analysis techniques, and variables to be used in the analysis will be introduced. After

• Oncology Letters
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• v.42 no.1
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• pp.453-460
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• 2019

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer-specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined. We identified 22 CpG island loci that were commonly hypermethylated in ccRCC. Kaplan-Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan-Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein-coupled receptor 149 (GPR149), was significantly associated with shorter time-to-progression. Multivariate Cox regression models revealed that hypermethylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and GPR149 (HR, 7.07; P<0.001) may be independent predictors of tumor progression. Similarly, the methylation status of these two genes was significantly associated with poor outcomes in the independent external validation cohort. Collectively, the present study proposed that the novel methylation markers ZNF492 and GPR149 could be independent prognostic indicators in patients with ccRCC.

• Journal of Christian Education in Korea
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• v.76
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• pp.259-280
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• 2023

Purpose of study: This study aims to analyze the educational use of metaverses among pre-service nursing teachers at a university and explore the implications of designing and operating effective metaverse lessons. Research content and method: This study collected and analyzed data on the experiences and perceptions of 32 pre-nursing teachers enrolled in J University, a very small Christian-based university in Jeonju, Jeollabuk-do, Korea, who participated in a class using metaverses. And based on this, we analyzed the advantages, difficulties, and improvements of the class, differences from classes using Zoom, impressions of the class, and suggestions for effective classes. Conclusions and Suggestions: As a result of analyzing various aspects of perceptions and experiences of classes utilizing the metaverse, it was found that in order to conduct effective classes utilizing the metaverse, it is necessary to check the infrastructure for communication and devices before class, select a metaverse platform according to the goals and contents of the course, and build a space for educational activities. In addition, it was found that it is necessary to provide guidance on how to use the metaverse and conduct sufficient training before running classes with learner-centered teaching methods. In the future, it is expected that systematic research on the principles and teaching-learning models of classroom design using the metaverse will continue to be conducted.

• Journal of Chest Surgery
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• v.57 no.5
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• pp.460-466
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• 2024

Background: Numerous studies have investigated methods of predicting postoperative pulmonary complications (PPCs) in lung cancer surgery, with chronic obstructive pulmonary disease (COPD) and low forced expiratory volume in 1 second (FEV₁) being recognized as risk factors. However, predicting complications in COPD patients with preserved FEV₁ poses challenges. This study considered various diffusing capacity of the lung for carbon monoxide (DL_CO) parameters as predictors of pulmonary complication risks in mild COPD patients undergoing lung resection. Methods: From January 2011 to December 2019, 2,798 patients undergoing segmentectomy or lobectomy for non-small cell lung cancer (NSCLC) were evaluated. Focusing on 709 mild COPD patients, excluding no COPD and moderate/severe cases, 3 models incorporating DL_CO, predicted postoperative DL_CO (ppoDL_CO), and DL_CO divided by the alveolar volume (DL_CO/VA) were created for logistic regression. The Akaike information criterion and Bayes information criterion were analyzed to assess model fit, with lower values considered more consistent with actual data. Results: Significantly higher proportions of men, current smokers, and patients who underwent an open approach were observed in the PPC group. In multivariable regression, male sex, an open approach, DL_CO <80%, ppoDL_CO <60%, and DL_CO/VA <80% significantly influenced PPC occurrence. The model using DL_CO/VA had the best fit. Conclusion: Different DL_CO parameters can predict PPCs in mild COPD patients after lung resection for NSCLC. The assessment of these factors using a multivariable logistic regression model suggested DL_CO/VA as the most valuable predictor.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.87-101
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• 2024

This study aims to provide insights for the efficient utilization of resources by analyzing the entrepreneurship support efficiency of Korean universities. To identify the factors influencing the number of entrepreneurs, which is the primary goal of university entrepreneurship support, a multiple regression analysis was conducted, identifying five effective independent variables. Using these five identified independent variables as input variables and the number of entrepreneurs as the output variable, the DEA method was used to analyze the efficiency of entrepreneurship support for each university as of 2023. The analysis of 150 four-year universities in Korea showed that nine universities exhibited complete efficiency in both CCR and BCC models. Among the remaining 141 universities that showed inefficiency, the cause was scale for five universities, technology for two universities, and both scale and technology for 134 universities. Regarding the returns to scale, nine universities exhibited CRS, 79 exhibited IRS, and 62 exhibited DRS. Additionally, reference groups that could serve as benchmarks for improving the efficiency of inefficient universities were identified, and target values(projections) for each variable to achieve efficiency were also presented. Despite the limitations of the DEA model, this study helps each university identify the causes of inefficiency in their entrepreneurship support and derive specific improvements to enhance efficiency. This facilitates more efficient resource management and can positively impact the ultimate goals of university entrepreneurship support, such as regional economic development and job creation.

• Journal of the Korean Regional Science Association
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• v.40 no.3
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• pp.23-42
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• 2024

This study examines changes in housing insecurity among young adults from a longitudinal perspective, recognizing the dynamic nature of young adulthood. The objective of the study is to explore shifts in housing insecurity and to identify the factors affecting entry into and exit from housing insecurity. Using data from the Seoul Youth Panel in 2021 and 2022, housing insecurity is measured across three dimensions, and changes over one year are categorized. The sample consists of 40% of individuals experiencing persistent security, 33% experiencing persistent insecurity, 14% exiting insecurity, and 13% entering security, indicating that the transition into and out of housing insecurity is quite dynamic. Empirical results from the logistic regression models reveal several key findings. Firstly, crises in employment and social domains significantly correlate shifts in housing insecurity among young people. Unstable employment and unsatisfactory job conditions increase the risk of entering, and decrease the likelihood of exiting housing insecurity. Social isolation and lower social support increase the risk of entry into housing insecurity, while higher social support enhances the likelihood of exiting housing insecurity. Secondly, residential characteristics play a pivotal role in the transition of housing insecurity. Those living in non-apartments and renters are considerably less likely to exit housing insecurity compared to those living in apartments and homeowners, respectively. Furthermore, residing in rooftop or semi-subterranean location, or undergoing residential moves, significantly elevate the risk of entering housing insecurity. Thirdly, external supports appear to have a limited role in achieving housing security for young adults. Parental economic resources significantly facilitate exiting housing insecurity, whereas governmental housing policy benefits show no significant effect. These findings provide important implications for policy-making aimed at addressing and preventing housing insecurity among young adults.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.269-288
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• 2024

In the era of digital transformation, discussions about digital curation have become increasingly active not only in academia but also in various fields. The primary purpose of this study is to analyze the conceptual changes in digital curation over time, particularly by examining the definition statements related to digital curation as described in academic literature. To achieve this, academic research papers from 2009, when the term "digital curation" was first mentioned, to 2023 were collected, and definition statements that explained relevant concepts were extracted. Basic statistical analyses were conducted. Using DMR topic modeling and word networks, the relationships among keywords and the changes in their importance over time were examined, and a conceptual map of digital curation was made focusing on the main topics. The results revealed that the concept of digital curation is primarily centered around the themes of "data preservation," "traditional curator roles," and "product recommendation curation." Depending on the researchers' intentions for utilizing digital curation, the concept was expanded to include topics such as "content distribution and classification," "information usage," and "curation models." This study is significant in that it analyzed the concept of digital curation through definition statements reflecting the perspectives of researchers. Additionally, the study holds value in explicitly identifying changes in the concepts that researchers emphasize over time through the trends in topic prevalence.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.59-88
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• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.