• Korean Journal of School Psychology
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• v.18 no.1
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• pp.31-48
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• 2021

The purpose of this study was to determine whether the retention of university freshmen could be predicted using their peer relationships in a specific department. In this study, retention was defined as a student staying enrolled in their university for a certain period of time. Social relationships are formed through interaction between people, so both students' self-perceptions and others' perceptions of them must be accounted for, so we used a social network analysis that did so. We examined social networks visualizations that allowed for a rich interpretation of numerical information. Participants in this study were freshmen who enrolled in an undergraduate program in 2017, 2018, or 2019. We used the name generator method to determine how quantitative friendship network variables predicted the academic retention up to the first semester of 2020. Cox proportional hazard model analysis showed that the weighted indegree centrality with intimacy positively predicted retention. The results of this study can be used to identify and conduct interventions for students who may be likely to disenroll. However all of the students did not participate in the department, it was difficult to examine their entire peer networks. Thus, this study's results cannot be generalized because the participants are students of a specific major, so further research is needed to produce more generalizable results.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1472-1484
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• 2019

This study aimed to verify the effects of nutritional status, K-ADL, K-IADL, and social network on the life satisfaction of the elderly in home. Total 213 research subjects participated in this study, and their average age was 71.38±5.59. As the methods of analysis, using the SPSS 21.0, this study examined the differences between variables in accordance with the general characteristics, and then verified the correlations between independent variables of nutritional status, K-ADL, K-IADL, social network(family networks, friends networks), and life satisfaction. In order to verify the factors having effects on the life satisfaction of the elderly in home, the stepwise multiple regression analysis was conducted. In the results of this study, in the general characteristics, the life satisfaction showed statistically significant differences in accordance with education(F=5.280, p=.002), economic condition(F=22.407, p<.001), monthly income(F=3.181, p=.015), and subjective health status(F=14.933, p<.001). In the results of verifying the correlation between independent variables, the life satisfaction showed positive correlations with family networks(r=268, p<.001) and friends networks(r=.286, p<.001) while the nutritional status(r=-.222, p=.001), K-IADL(r=-.235, p=.001), and interdependent social support(r=-.283, p<.001) showed negative correlations. The predictive factors on the life satisfaction of the elderly in home included the economic condition(β=.358, p<.001), subjective health status(β=.245, p<.001), interdependent social support(β=-.158, p=.009), and K-IADL(β=-.153, p=.012), and the explanatory power was 30.1%. The regression model was statistically significant(F=23.778, p<.001). Based on such results of this study, it would be necessary to develop programs that could maintain and improve the health of the elderly, and also provide financial support to the elderly suffering from economic hardship, in order to improve the life satisfaction of the elderly in home. Moreover, there should be the concrete measures for vitalizing the community-connected activities for interdependent social support.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.13-25
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• 2016

High-speed railway (Korea Train Express) has had a deep impact on the regional economy of Korea. Current high-speed rail research is mostly theoretical, there is a lack of quantitative research using a precise algorithm to study the effect of high-speed railway on the regional economy. This paper analyses the influence of high-speed rail on the regional economy, with a focus on the Daegu area. Quantitative analysis using department store indexes and regional medical records is performed to calculate the economic influence of high-speed rail. The result shows that high-speed railway effects the regional economy though regional consumption growth and medical care trends.

• Journal of agricultural medicine and community health
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• v.47 no.4
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• pp.242-254
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• 2022

Purpose: This study was designed to test structural equation modeling of the quality of life of elderly diagnosed dementia living in the community in order to provide guidelines for development of intervention and strategies to improve their quality of life. Methods: The participants in the study were elderly who visited the public health center in C rural between May 30 and september 15, 2017. Data collection was carried out through one-on-one interviews. Demographic factors, knowledge, Attitude, Self-Efficacy, social support, accessibility, request for Information, health practice, depression, subjective memory complaints, dependence scale and quality of life were investigated. Results: The final analysis included 192 elderly. Fitness of the hypothesis model was appropriate(χ²=192.89, p=.000, GFI=0.90, SRMR=0.08, NNFI=0.94, CFI=0.95, PNFI=0.72, RMSEA=0.07). Depression, subjective memory complaints and dependence were found to be significant explaining varience in quality of life. Social support, dementia preventive behavior and health practice had an indirect effect on the quality of life. Conclusions: To improve the quality of life of elderly diagnosed dementia living in the community, comprehensive interventions are necessary to manage knowledge, attitude, self-efficacy, social support, health practice, depression, subjective memory complaints and dependence that can contribute to enchance the quality of life of elderly diagnosed dementia living in the community.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.155-166
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• 2015

This study selected container terminals of Gwangyang and Busan Ports to evaluate the influence of efficient container terminals. For the study, after data envelopment analysis (DEA) using the CCR and BCC models, the decision-making unit (DMU) system was used to define nodes; and with the use of a reference group in DEA (BCC model) and a lambda value, this study created a social network and analyzed the influences of efficient DMUs through a centrality analysis of eigenvectors. The results are presented as follows: First, as a result of the DEA, CCR efficiencies in PNC, HJNC, and HPNT container terminals of Busan Port were 1 and BCC efficiencies at Singamman Terminal, Wooam Terminal, PNC, HJNC, HPNT, and BNCT container terminals of Busan Port were 1. Second, as a result of undertaking social network analysis (SNA), according to an eigenvector centrality analysis, HJNC Terminal was referred to the most (influence score of 0.515), which indicates that it is the most influential as a container terminal. The influence of PNC Terminal was 0.512, while that of Wooam Terminal was 0.379. CJ Korea Express in Gwangyang Port was ranked fourth in influence, but its influence score of 0.256 indicates that it was the most influential of the container terminals at Gwangyang Port.

• The Journal of Information Systems
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• v.18 no.3
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• pp.47-70
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• 2009

Social network is one of the representative analytical method of the complexity theory and this research analyzed various and unique strategic alliance model of e-business domain using social network technique. A lot of small and medium firms of e-business field had developed many useful type of strategic alliances for the firms tried to maximize the effect of advertisement, marketing and to make up for their weak points and to compete with huge company with capital strength long before. But it is too rare to analyze the structure of the firm networks and to study the evolution and extension of business model considered the role of each company in the network. Social network analysis helps each firm's network easily visualized and completely modelized. Additionally, this paper cries to analyze the relationship between the role of hub and broke in the firm networks for strategic alliance, and financial performance. We demonstrate the firm with finer business model to the business environment can make higher financial performance. This implies that the firm that can create new finer business model, will lead the network of e-business firms and evolve the industry of e-business.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.364-387
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• 2009

Female marriage immigrants have increased since the mid-1990s in Korea. Thus, their adaptation to Korean society has been a big social issue in Korea. There are a lot of academic researches on their adaptation to Korean society so far. We cannot sufficiently understand general main factors of their adaptation to Korean society because of methodological problems in the studies on the female marriage immigrants. Particularly, there are very few studies on female marriage immigrants' adaptation to their communities. This study analyzes determinants of female marriage immigrants' adaptation to their communities, using stepwise multi-regression. Data are collected from questionary survey on female marriage immigrants in Seoul, Gyeong-gi, Daegu, Gyeong-buk, Gwangju, Jeon-nam. Dependent variables are community life, spatial cognition and activities, and neighborhood relationship. Each dependent variable is analyzed with 30 independent variables through stepwise multi-regression. As a result, 16 positive determinants and 2 negative ones are selected. Positive determinants are resident identity, age, adaptation to home, number of Korean friends and same nationals' friends in Korea and so on. But, Korean language fluence is not selected as a significant factor. This is different from a general recognition. As exiting researches, the importance of social network and adaptation variables is also identified in this study.