Factors Influencing Subjective Happiness Index of Health behavior, Smart phone addiction, Suicidal Ideation among College students (건강행태, 스마트폰중독 및 자살생각지수가 주관적 행복지수에 미치는 영향)
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- Journal of Digital Convergence
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- v.11 no.10
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- pp.557-569
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- 2013
This study was conducted to identify factors related to subjective happiness Index and by analyzing students, health behavior, smart phone addiction, suicidal Ideation, and in order to develop appropriate measure tp prevent suicide among university students. Methods: From November 19, 2012 until December 14, 2012, 300 college students from one of the iniversities located at K city were surveyed. Data were analyzed using with SPSS window 18.0. Results: The mean score for college students' subjective happiness Index was 4.58 on a scale of 7. There were significant differences on the Subjective happiness Index for the following areas: sex, parental survival & living and household monthly income. There was a negative correlation among the subjective happiness Index, smart phone addiction, suicidal Ideation. Conclusion: Results indicate that factors influencing Subjective happiness are smart phone addiction, suicidal ideation. We suggest that need to establish policies providing family support, smartphone addiction prevention program suicide prevention program to improve a level of happiness.
Purpose - Despite the importance of price, many companies do not implement pricing policies smoothly, because typical price management strategies insufficiently consider logistics efficiency and an increase in logistics costs due to logistics waste. This study attempts to examine the effect of product line pricing, which corresponds to product mix pricing, on logistics efficiency in the case of manufacturer A, and analyzes how logistics performance changes in response to these variables. Research design, data, and methodology - This study, based on the case of manufacturer A, involved research through understanding the current status, analyses, and then proposing improvement measures. Among all the products of manufacturer A, product group B was selected as the research object, and its distribution channel and line pricing were examined. As a result of simulation, for products with low loading efficiency, improvement measures such as changing the number of bags in the box were suggested, and a quantitative analysis was conducted on how these measures influence logistics costs. The TOPS program was used for the Pallet loading efficiency simulation tool in this study. To prevent products from protruding out of the pallet, the maximum measurement was set as 0.0mm, and loading efficiency was based on the pallet area, and not volume. In other words, its size (length x width) was focused upon, following the purpose of this study and, then, the results were obtained. Results - As a result of the loading efficiency simulation, when the number of bags in the box was changed for 36 products with low average loading efficiency of 73.7%, as shown in