• Journal of Korean Academy of Nursing
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• v.47 no.4
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• pp.514-525
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• 2017

Purpose: The purpose of this study was to construct and test a structural equation model for Diabetes self-management (DSM) behavior and Quality of life (QoL) in older adults with diabetes who use Citizen Health Promotion Centers. The theory used this study was a combination of the Information-Motivation-Behavioral Model (IMB) and Self-Determination Theory (SDT) to reflect autonomous characteristics of participants. Methods: Data were collected from April 20 to August 31, 2015 using a self-report questionnaire. The sample was 205 patients with type 2 Diabetes who regularly visited a Citizen Health Promotion Center. SPSS 22.0 and AMOS 22.0 programs were used to analyze the efficiency of the hypothesized model and calculate the direct and indirect effects of factor affecting the participants' DSM behavior and QoL. Results: The supported hypotheses were as follows; 1) The variable that had a direct effect on QoL was health behavior adherence (${\gamma}=.55$, p=.007). 2) The variables that had a direct effect on DSM behavior were DSM information (${\gamma}=.15$, p=.023), DSM confidence (${\gamma}=.25$, p<.001), and autonomous motivation (${\gamma}=.13$, p=.048). 3) The variable that had a direct effect on DSM confidence was autonomy support (${\gamma}=.33$, p<.001). Conclusion: The major findings of this study are that supporting patient's autonomous motivation is an influential predictor for adherence to DSM behavior, and integrative intervention strategies which include knowledge, experience and psychosocial support are essential for older adults with diabetes to continue DSM behavior and improve QoL.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.413-420
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• 2013

Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.469-482
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• 2014

This study developed a Bayesian spatial regional frequency analysis, which aimed to analyze spatial patterns of design rainfall by incorporating geographical information (e.g. latitude, longitude and altitude) and climate characteristics (e.g. annual maximum series) within a Bayesian framework. There are disadvantages to considering geographical characteristics and to increasing uncertainties associated with areal rainfall estimation on the existing regional frequency analysis. In this sense, this study estimated the parameters of Gumbel distribution which is a function of geographical and climate characteristics, and the estimated parameters were spatially interpolated to derive design rainfall over the entire Han-river watershed. The proposed Bayesian spatial regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis, and even better performance in terms of quantifying uncertainty of design rainfall and considering geographical information as a predictor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.142-145
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• 2017

Users of Smartphone in Korea are using the majority of the economically active population over 99% and experts have seen that they have reached saturation after the initial formation stages. The purpose of this study is to investigate the influencing factors of dominant design attributes on the intention of continuous use of Smartphone users. Predictor factors were selected perceived usefulness and perceived ease of use suggested on extended the technology acceptance model. The concept model was completed by selecting the dominant design attribute as a mediator. Participants of this study were 150 Smartphone users in Busan Gyeongnam and Iksan Jeonbuk province in accordance with convenience sampling. IBM SPSS Statistics 19 were employed for descriptive statistics, Smart PLS (partial least squares) was employed for confirmatory factor analysis and path analysis of casual relationship among variables and effect. Analytical results show that all paths of continue usage intention are significant. This study suggests practical and theoretical implications based on the results.

• Journal of Fashion Business
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• v.19 no.2
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• pp.69-84
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• 2015

The objective of this study is to investigate the intention to adopt smart wear, based on extended UTAUT model. We examined the effects of performance expectancy (PE), effort expectancy (EE), hedonic motivation (HE), social influence (SI), facilitating conditions (FC), and price value (PV) on the intended adoption of smart watch and smart shoes, respectively. In addition, moderating effects of gender, age, and innovation resistance were examined. An online survey was conducted, comprised of 2030 consumers who were aware of smart watch or smart shoes. In total, 393 responses were analyzed. About 50.4% were male, and 44.8% were in their 20's. An exploratory factor analysis generated five factors - PE & HM, EE, SI, FC, and PV- which were employed as independent variables in the multiple regression models. PE & HM, PV, and SI influenced on the intention to use both smart devices. FC showed the significant effect only on the intention to adopt the smart watch. In terms of gender differences, SI and PV were the important predictors of the intention to adopt the smart watch in the female group only. With respect to age difference, SI was very effective in explaining the intention of individuals in their 30's to adopt smart wear. Among the low innovation resistance group, SI was significant predictor, while PE & HE and PV were significant among the high resistance group. The findings provide useful information about the possibility of the adoption of smart wear, and new insight into market segmentation.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.55-82
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• 2020

This study examined the factors that influence the turnover intention, job performance of call center representatives based on the adaptive structuration theory (AST). This study intended to empirically examine how individual characteristics of representative affect the technological and task adaptation, how they affect job performance and turnover intention. On the other hand, this study also explains how rational culture and organization a reputation which are considered as dimensions of organizational characteristics affects organizational commitment, and verifies the relationship between organizational commitment and job performance and turnover intention. Finally this paper aim to provide academic and practical implications. In order to solve the above research problems, this research proposed a model based on the adaptive structuration theory. In order to identify the relationship between the proposed variables and the AST for individual, we conducted an empirical test on the call center representatives. The structural equation model was used to verify the research model and hypotheses. The results of the empirical analysis show that the personal characteristics of counselors, such as communication skills, multitasking abilities, and innovativeness have a positive effect on skill adaptation, and skill adaptation has a positive effect on task adaptation, furthermore, it influences on job performance and turnover intention Respectively. In addition, among the factors of organizational environmental dimensions of the call center, it was found that organizational reputation not only increase continuance commitment but also increase normative commitment. Contrary to our expectations, perceived rational culture didn't have a positive effect on organizational commitment. Also, continuance commitment and normative commitment are valid predictors of job performance, but they have nothing to do with turnover intention. On the contrary, emotional commitment is the only one variable among three dimensions of organizational commitment have a positive effect on turnover intention, but is not a valid predictor of job performance.

• Asia Marketing Journal
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• v.18 no.2
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• pp.63-93
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• 2016

As one of the most popular forms of electronic commerce, cross-border e-commerce provides numerous consumer benefits, such as broad and deep product assortments at low prices. However, consumers tend to exhibit high involvement in cross-border e-commerce purchases due to high risks associated with such purchases. The paper focuses on identifying causal relationships between e-commerce website traits (i.e., website trustworthiness, interactivity and convenience) and consumer satisfaction and along with loyalty. We proposed a reflective-reflective hierarchical model (first-order reflective and second-order reflective model) and used the Partial Least Square Analysis Statistical method to test the hypotheses. The results demonstrated that website trustworthiness, convenience and interactivity were all positively related to consumer satisfaction. Also, higher satisfaction led to stronger customer loyalty, which may well increase revisit intentions. We also compared the strength of each path from a website trait to satisfaction. Results illustrated that the path from website convenience to satisfaction is the strongest among the three website traits. In addition, we separately examined differences within Korean group and Chinese group. No statistically significant difference among website traits was found within Korean group. However, within Chinese group, we found that website convenience is the most important predictor of satisfaction. This indicates that Chinese consumers are more concerned about the website convenience than interactivity and trustworthiness when they make cross-border e-commerce purchases. Moreover, this study investigated possible differences between Korean and Chinese group. We used multi-group analysis of Smart PLS 3.0 to test the results. It was shown that the two groups do not display statistically significant difference in trustworthiness, interactivity, or convenience in influencing customer satisfaction. Finally, we presented further implications which are useful for understanding of the proposed model. Limitations and improvements of this research were presented, too.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.131-143
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• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.

• Ocean Science Journal
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• v.43 no.4
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• pp.175-182
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• 2008

Bottlenose dolphins are the only cetaceans regularly observed in the northern Adriatic Sea, but they survive at low densities and are exposed to significant threats. This study investigates some of the factors that influence habitat use by the animals in a largely homogeneous environment by combining dolphin data with hydrological and physiographical variables sampled from oceanographic ships. Surveys were conducted year-round between 2003 and 2006, totalling 3,397 km of effort. Habitat modelling based on a binary stepwise logistic regression analysis predicted between 81% and 93% of the cells where animals were present. Seven environmental covariates were important predictors: oxygen saturation, water temperature, density anomaly, gradient of density anomaly, turbidity, distance from the nearest coast and bottom depth. The model selected consistent predictors in spring and summer. However, the relationship (inverse or direct) between each predictor and dolphin presence varied among seasons, and different predictors were selected in fall. This suggests that dolphin distribution changed depending on seasonal forcing. As the study area is relatively uniform in terms of bottom topography, habitat use by the animals seems to depend on complex interactions among hydrological variables, caused primarily by seasonal change and likely to determine shifts in prey distribution.

• The Journal of Korean Academic Society of Nursing Education
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• v.19 no.1
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• pp.16-23
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• 2013

Purpose: The purpose of this study was to examine the effects of intrinsic motivation and e-learning strategies on nursing students' learning satisfaction in blended e-learning environment. Method: The survey was performed with 111 undergraduate nursing students who have taken community health nursing in 1 university. The data was collected by questionnaires and were analyzed with the IBM SPSS statistics 20.0, using descriptive statistics, Pearson's correlation coefficient and multiple regression. Results: The mean score for intrinsic motivation was 3.25, for e-learning strategies, 3.56, and for learning satisfaction, 3.69. Significant positive correlation among intrinsic motivation, e-learning strategies, and learning satisfaction. The regression model explained 41.4% of learning satisfaction. Both intrinsic motivation and e-learning strategies were significant predictor of learning satisfaction. Conclusion: In order to improve the learners' learning achievement and learning satisfaction in blended e-learning environment, intrinsic motivation should be strengthen and e-learning strategies should be developed.