• Journal of Information Technology Services
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• v.18 no.2
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• pp.17-35
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• 2019

The objective of this study is to extract affecting factors on game satisfaction of a mobile game through the two mediated variables, self-fulfillment and flow. To apply mobile environment changed into the research model of a mobile game, we classify independent factors into three types such as system characteristics, cognitive characteristics, and individual characteristics. The previous studies for using mobile game have been based on intrinsic motivation, such as flow, as a mediated factor. However, the flow variable has some limitations in explaining user satisfaction aspects of mobile game, and the self-fulfillment variable is popularly used as an important mediated factor in the field of sports science. Because users of mobile game often attain their goals as sports event, we utilize self-fulfillment as well as flow as mediated variables. We developed a research model that includes independent factors, mediated variables of self-fulfillment and flow, and a dependent variable of game satisfaction. The data were collected from 438 users of mobile game and were used for analysis. Based on the survey results, we found the followings: (1) Immediate connectivity, enjoyment, relationship, early adopter tendency, and competitiveness affect self-fulfillment, and self-fulfillment affects the flow. (2) Enjoyment and competitiveness affect self-fulfillment, flow, and game satisfaction simultaneously.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.263-268
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• 2022

We derive an alternative proof of Marsaglia's method for generating a pair of independent standard normal random variables.

• Journal of Venture Innovation
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• v.2 no.2
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• pp.1-19
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• 2019

Various studies have been conducted on factors influencing the adoption of new technologies. In particular, the factors influencing customers' acceptance of new technologies that emerged along with the arrival of the 4th Industrial Revolution. Theoretical models are mainly based on the technology acceptance model (TAM) or Extended Unified Theory of Acceptance and Use of Technology : UTAUT2). However, there are no trust variables in these models. In this study, we added trust variable and tested augmented reality(AR). Three types - trust as an independent variable, as a mediating variable, and as a moderating variable - were analyzed statistically. As a result, trust was most explanatory when viewed as a mediating variable between independent variables and intention to use. In theory, it has been shown that trust acts as a mediating effect. In practice, trust is most important in accepting new technologies, so it is important to prioritize trust.

• Journal of the Korean housing association
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• v.20 no.4
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• pp.69-78
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• 2009

The purpose of this study is to identify which is the better indicator to forcast housing tenureship between permanent income and current income, and study the effects of non-human capital asset on housing tenureship. To forcast permanent income, a statistic regression equation is used with current income as the dependent variable. Multi-nomial logistic model is used to forcast the housing tenureship Using current income as the dependent variable delivered a more accurate result than using permanent income. Current income is used as a dependent variable and sex, age, education and occupation are used as independent variables to forcast permanent income. Non-human capital asset is also used as an independent variable. Also, excluding non-human capital asset variable when forcasting bothe permanent income and housing tenureship proved to be more accurate. Because permanent income, the sum of future income and current asset, is a good indicator of current consumption including housing, the result with permanent income should be more accurate than the forcast using current income. This implies an underdevelopment of a housing mortgage system that enables people to consume now on the basis of their future income. The Korea's unique Chonsei housing rental system has also made it difficult to forcast housing tenureship based on people's permanent income and asset. While, the Key-money of Chonsei housing and the housing asset of homeowners with debt are very similar in their amount, the result is completely different. One is a renter and the other is a homeowner.

• Journal of Environmental Health Sciences
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• v.17 no.1
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• pp.95-103
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• 1991

For the purpose of estimating the working environment and the relationship between the airborne lead concentration and the ZPP level in the whole blood of the workers, the airborne lead concentrations and the ZPP level were measured at the 26 plants which deal with lead, from October 5 to November 5 in 1988. Analysis of the airborne lead concentration was performed by NIOSH Method 7082, and the ZPP level was measured by a hematofluorometer. The following results are concluded. 1. The average airborne lead concentration of the lead battery manufactures is 0.025mg/m$^{3}$ and that of the secondary lead smelters is 0.023mg/m$^{3}$. There were no significant differences between industry (p>0.1) 2. At the lead battery manufacture, the process of lead powder production showed the highest concentration of 0.034mg/m$^{3}$ but there were no significant differences among the processes (p>0.1). At the secondary lead smelter, the process of dismantling waste batteries showed the highest concentration 0.141mg/m$^{3}$, and there were very significant differences among the processes (p<0.005). 3. The ZPP level in the whole blood showed significant differences between industry (p<0.10). The average ZPP level of the lead battery manufactures is 133.0 + 106.3 $\mu$g/100ml and that of the secondary lead smelters is 149.6 + 110.9 $\mu$g/100ml. 4. The correlation coefficients between the airborne lead concantration and ZPP level were 0. 426 (p<0.001) for the lead battery manufactures and 0.484 (p<0.001) for the secondary lead smelters. The correlation coefficients between the work duration (in months) and the ZPP level were 0.238 (p<0.001) for the lead battery mannfactures and 0.075 (p>0.10) for the secondary lead smelters. 5. The linear regression equation, with the airborne lead concentration as an independent variable and the ZPP level as a dependent variable, is Y=96.84+1300.34X (r=0.448, p<0.001) for the 26 plants which deal with lead. The linear regression equation, with the work duration(in months) as an independent variable and the ZPP level as a dependent variable, is Y=127.28 +0.49X (r=0.162, p<0.05). 6. The correlation coefficients between the amount of inhaled lead and ZPP level were 0.349 (p < 0.001) for the lead battery manufactures and 0.318(p<0.001) for the secondary lead smeltes. The linear regression equation for the 26 plants surveyed, with the amount of inhaled lead as an independent variable and ZPP level as a dependent variable, is Y=123.63+18.82X (r=0. 335, p<0.001).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.31-39
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• 2017

The goal of this study was to efficiently analyze the relationships of the number of thefts with related factors, considering the spatial patterns of theft crimes. Theft crime data for a 5-year period (2009-2013) were collected from Haeundae Police Station. A logarithmic transformation was performed to ensure an effective statistical analysis and the number of theft crimes was used as the dependent variable. Related factors were selected through a literature review and divided into social, environmental, and defensive factors. Seven factors, were selected as independent variables: the numbers of foreigners, aged persons, single households, companies, entertainment venues, community security centers, and CCTV (Closed-Circuit Television) systems. OLS (Ordinary Least Squares) and GWR (Geographically Weighted Regression) were used to analyze the relationship between the dependent variable and independent variables. In the GWR results, each independent variable had regression coefficients that differed by location over the study area. The GWR model calculated local values for, and could explain the relationships between, variables more efficiently than the OLS model. Additionally, the adjusted R square value of the GWR model was 10% higher than that of the OLS model, and the GWR model produced a AICc (Corrected Akaike Information Criterion) value that was lower by 230, as well as lower Moran's I values. From these results, it was concluded that the GWR model was more robust in explaining the relationship between the number of thefts and the factors related to theft crime.

• Journal of Korean society of Dental Hygiene
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• v.12 no.1
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• pp.213-223
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• 2012

Objectives : The purpose of this study is to identify relationships between turnover intention and variable factors among dental hygienists. Methods : Each factor of validity and reliability were investigated using factor analysis and Cronbach's ${\alpha}$. The hypothesis of the data was proven and analyzed using the pearson correlation, regression analysis, and analysis of variance (ANOVA). Results : The independent variables were based on self-efficacy, the dependent variables were based on job satisfaction. The results of the regression analysis were that self-efficacy is as high as job satisfaction. Independent variables based on self-efficacy and job satisfaction. The dependent variable is organizational commitment multiple regression analysis was used. The results showed that self-efficacy and job satisfaction are as high as organizational commitment. Independent variables based on organizational commitment. The dependent variable is the turnover-intention. Simple regression analysis showed that organizational commitment is as low as turnover-intention. Self-efficacy, job satisfaction and organizational commitment are different depending on individual traits and personal characteristics. Self-efficacy has been shown to have a positive relationship with job experiences, public work and turnover intention. Organizational commitment is also related with job experiences. However, there was no significant difference between job satisfaction, turnover intention and personal characteristics. Conclusions : According to the factor analysis of turnover intention, self-efficacy, job satisfaction, organizational commitment and turnover intention were correlated to each other. Self-efficacy, job satisfaction, and organizational commitment has a negative association with turnover intention.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.743-753
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• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).

• Communications of the Korean Mathematical Society
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• v.26 no.4
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• pp.669-684
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• 2011

In this note, we obtain the expression of the characteristic fucntion of the random variable $\int_o^TB_s^{{\alpha},{\beta}}dB_s^{H,K}$, where $B^{{\alpha},{\beta}}$ and $B^{H,K}$ are two independent bifractional Brownian motions with indices ${\alpha}{\in}(0,1),{\beta}{\in}(0, 1]$ and $HK{\in}(\frac{1}{2},\;1)$ respectively.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.239-250
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• 1994

In this paper, we prove a strong large deviations theorem for the ratio of independent randoem variables with error rate of $O(n^{-1})$. To obtain our results we use the inversion formula for the tail probability and apply the Chaganty and Sethuraman's (1985) approach.