• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.300-309
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• 2012

The purpose of this study was to understand about the influence of female undergraduates health behavior and health state upon sociality and the influence of sociality upon college life satisfaction. The subjects were 335 female junior-college students with major related to public health in Busan, Daegu, and Gyeongnam and were carried out questionnaire survey on general characteristics, sociality, college life satisfaction, health behavior, health status, and internet addiction. An analytical method was made through chi-square test, t-test, one-way ANOVA, ANOVA for trend, and structural equation modeling(SEM). As a result, the influence of accountability had the greatest influence as sub-factor. Accountability and age had a positive correlation(p<0.001). Accountability was low when self-cognitive body shape was very thin(p=0.005). In sociality, full age(p<0.001), self rated health(p<0.001), and physical education instruction for the 3rd grade of high school(p=0.004) showed direct effect. Also, sociality showed direct influence upon college life satisfaction. The older age, the higher self rated health, and the more instruction for the 3rd grade of high school led to the higher sociality, thereby having been indicated to be higher in college life satisfaction. Accordingly, a multi-dimensional effort is judged to be necessary for reinforcing physical education activity for high school students and for improving their quality of life as a plan for increasing college life satisfaction.

• Mood & Emotion
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• v.16 no.3
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• pp.152-157
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• 2018

Objectives : The purpose of this study was to examine psychosocial factors influencing suicidal ideation of community dwelling elderly, using the senior community center in Jeonnam Province. Methods : A total of 2,202 subjects (369 males, 1,833 females) were recruited. We evaluated sociodemographic factors, using a self-reporting questionnaire. Subjects completed the Geriatric Depression Scale-Short Form Korean Version (SGDS), Multi-dimensional Scale of Perceived Social Support (MSPSS), Korean version of the General Health Questionale-12 (GHQ-12) and Satisfaction with Life scale (SWLS), to assess psychosocial factors affecting suicidal ideation. Results : Among 2,202 subjects, 179 (8.1%) reported recent suicidal ideation. Self-perceptive health status (p<0.001) and physical disease (p=0.002) revealed differences between two groups. The scores of four scales in the suicidal group were significantly different from the control group: SGDS (p<0.001) and GHQ-12 (p<0.001) were higher, while MSPSS (p<0.001) and SWLS (p<0.001) were lower, in the suicidal ideation group than the control group. Multivariate logistic regression analysis revealed that physical disease (OR 2.575, 95%CI 1.022-6.492), SGDS (OR 1.181, 95%CI 1.120-1.246) and GHQ-12 (OR 1.192, 95%CI 1.108-1.283), were significantly associated with suicidal ideation. Conclusion : Findings support that physical disease, depression, and general mental health may correlate to suicidal ideation in the elderly.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.614-625
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• 2018

This study was conducted to investigate the level of resilience and the factors affecting resilience in nursing students, and to provide basic data to develop a program that improves resilience of nursing students. Data were collected from 155 nursing students in D city from April 15 to May 30, 2018 using the Resilience Scale, College Life Stress Scale, Self-Efficacy Scale (SES), Multi-Dimensional Scale of Perceived Social Support (MSPSS), Positive Affect Scale, and Stress Coping Strategy Scale. Data were analyzed using t-tests, ANOVA, Pearson's correlation coefficient, and multiple regression. The mean score for resilience in nursing students was 3.54 points. There was a significant difference in resilience according to academic score and satisfaction with major. Resilience was significantly correlated with self-efficacy, social support, and active stress coping strategy. Factors that impacted resilience in multiple regression were self-efficacy (${\beta}=0.39$, p<0.001), active stress coping strategy (${\beta}=0.26$, p<0.001), and social support (${\beta}=0.16$, p<0.001). These factors explained 51.0% of the variance in the resilience of nursing students. Therefore, it will be necessary to develop a program that can enhance resilience by strengthening protective factors such as self-efficacy, active stress coping strategies, and social support.