• Journal of Distribution Science
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• v.13 no.12
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• pp.113-131
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• 2015

Purpose - In today's era of globalization, the competitive power of enterprises is growing fiercer, calling for organizations to be able to respond flexibly to survive and maintain predominance in competition. In turn, keen competition exists among enterprises for the systematic management of members' knowledge to secure predominance in such competition. Under such circumstances, SMEs must find and utilize positive causes for change that affect organizational performance. The objective of this study is to analyze the structural relationship between four factors known from prior research-a CEO's positive leadership, members' positive life positions, learning organization activities, and job engagement-and organizational performance. Research design, data, and methodology - To achieve this objective, this study established the following four research problems. First, do CEOs' positive leadership, members' positive life positions, learning organization activities, and job engagement affect organizational performance? Second, do CEOs' positive leadership, members' positive life positions, and learning organization activities affect job engagement? Third, do CEOs' positive leadership and members' positive life positions affect learning organization activities? Fourth, does CEOs' positive leadership affect members' positive life positions. Additionally, to achieve the objective of this study, the research model was selected on the basis of a documentary survey of 787 full-time employees at 100 SMEs, which was used to collect related data. Results - The following conclusions were drawn. First, a CEO's positive leadership directly affects members' positive life positions, learning organization activities, and job engagement. Second, positive leadership only indirectly affects organizational performance. That is, positive leadership has an indirect effect on organizational performance given the parameters of members' positive life positions, learning organization activities, and job engagement. Third, members' positive life positions directly affect learning organization activities and job engagement, but indirectly affect organizational performance with learning organization activities and job engagement as parameters. Fourth, learning organization activities directly affect job engagement and organizational performance. Additionally, learning organization activities indirectly affect organizational performance with job engagement as a parameter. Fifth, job engagement directly affects organizational performance. Conclusions - A CEO's positive leadership and members' positive life positions do not directly affect organizational performance but have a positive effect through learning organization activities and job engagement. In particular, CEOs' positive leadership was proven to be the major factor to affect members' positive life positions, learning organization attitudes, and job engagement, and learning organization activities and job engagement were found to be major factors that directly affect organizational performance. Considering these conclusions, the direct effect of a CEO's positive leadership on organizational performance is not statistically significant but seems to affect members' positive life positions, learning organization activities, and job engagement, which ultimately affects organizational performance. In addition, CEOs' positive leadership is an important factor that enhances the factors with the strongest effect on organizational performance-activities of learning organizations and job engagement.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2004.06a
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• pp.47-52
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• 2004

이 연구는 이미 편성되어 있는 초등학교 학급의 학생들을 연구 대상으로 학생들의 개인적 특성을 장 독립-장 의존 인지양식으로 분류하여 이에 따른 e-learning 시스템의 학습효과를 분석하기 위해 실험연구를 실시하였다. 연구결과 e-learning을 통한 학습은 인지양식에 따라 학습효과에 상이한 결과를 가져오기 때문에 e-learning 학습 컨텐츠 개발과정에서 보다 구체적으로 고려되어야 할 사항임을 시사하고 있다.

• The Journal of Information Systems
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• v.17 no.4
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• pp.99-127
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• 2008

E-learning becomes an important part of education these days in Korea. Students had no choice but to accept the e-learning when the e-learning was used as a supplementary learning tool within a traditional class or a stand-alone distance education. This study focuses on the students' intention of e-learning acceptance for voluntary studies. In voluntary study, students have the rights to adopt the e-learning or not for their personal study. We used individual characteristics as the external variables of TAM to explain user acceptance of e-learning for voluntary studies and examined the effect of individual characteristics on user's beliefs. Research model and nine hypotheses were set up to identify the relationships among these variables based on investigations of previous researches. The theoretical model is tested with questionnaires from 420 users who accept e-learning for voluntary studies. We tested the measurement and research models by applying a structural equation modeling(SEM) approach, using the AMOS 5.0. Overall, the results provided support for the model as explaining acceptance of an e-learning system. Most path coefficients in the research model were found statistically significant. The results showed usefulness and enjoyment and subject norm were the factors affecting attitude of students using e-learning. In addition, usefulness and subject norm were the factors affecting intention of students using e-learning. The results show innovation and self-efficacy have a significant impact on user's perception of ease of use. Self-efficacy also have significant effects on user's perception of usefulness.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.283-289
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• 2019

This study classifies the characteristics of e-learning learners into personal and psychological factors and examines how they affect the e-learning effects and intention to reuse. For this purpose, a research study was conducted targeting university students at Y and K universities in Gyeongsangbuk-do. As a result, first, the grade, self-control, concentration and self-efficacy of the learner influenced the learning effect of e-learning. Second, learner's grade, e-learning experience, self-efficacy, and learning effect were found to influence e-learning intention. Therefore, the system design and the managerial role of the instructor should be considered in order to develop various contents considering the characteristics of university students and to manage the learning process by themselves. The results of this study are expected to provide effective data for improving the learning effect of e-learning and to provide basic data on individual characteristics of e-learning learners.

• Culinary science and hospitality research
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• v.20 no.5
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• pp.76-83
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• 2014

This article examines the effects of education service quality of culinary education facility on the learning outcome. For this, items for figuring out the relation between education service quality and the learning outcomes has been deducted through IPA regarding education service quality. Further, the factors of education service, which is for verifying the effects of relevant experiences (culinary certificate) have been examined according to advanced studies. Self administered questionnaires have been prepared by 231 samples in total and the collected data have been analyzed through frequency, factor, reliability and regression analyzing methods by using SPSS 18.0. The results from paired t-test performed on the importance and performance of culinary education service quality indicate that expertise, level of knowledge, education method of instructor and additional convenience facilities (locker and lounge etc) are the most important factors respectively. The results from IPA analysis show that the quadrant 1 includes knowledge level, education method, kindness of instructor. The quadrant 2 contains awareness of instructor while the third one includes culinary facility, tool structure and convenience facility. In addition the quadrant 4 includes hygiene of learning place. According to the results from hypothesis test, education service quality affects learning outcome and the experience (certificate) of learner from culinary education facility results in difference in education service quality and awareness of learning outcome therefore the hypothesis is partially verified.

• The Journal of Information Systems
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• v.19 no.3
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• pp.75-103
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• 2010

This study analyzes a model appling value in the mobile learning. We investigate the interaction, ease of use, hedonic value, and utilitarian value considering importance factors in the mobile learning, and the customer values are found to influence the customer satisfaction. We analysis a relationship between these factors and the customer satisfaction, and the relationship between the customer satisfaction and the loyalty. Finally, we research difference among gender of the satisfaction on the mobile learning. To fulfill this purpose, the research model was designed from literature review, and the structural equation modeling technique was used to evaluate hypotheses. The major results of this study are as follows. First, the ease of use, hedonic value, and utilitarian value positively influenced the satisfaction, and the satisfaction had positive influences on the customer loyalty. But the interaction didn't have positive influence on the satisfaction. Second, gender difference was found for some path coefficient. Male user's interaction and ease of use has effect on the satisfaction, and Female user's hedonic value and utilitarian value have influenced the satisfaction. When it consider the market share or the growth potential of the mobile learning industry, the results of this study will provide the useful information to manage mobile learning.

• Korean Management Science Review
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• v.26 no.2
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• pp.71-89
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• 2009

The purpose of this research is to identify the factors affecting user acceptance of e-learning site. To more precisely explain an individual's behavior of accepting e-learning site, Perceived Interactivity is divided into four components; User Control, Responsiveness, Personalization and Connectedness. This research investigates the causal relationship among four components and basic factors of TAM. This research uses structural equation modeling (SEM) to confirm the validity and analyzes the causal relationship of the suggested model. The results indicates strong support for the validity of proposed model with 54.8% of the variance in behavioral intention to e-learning site. The result finds that all the basic casuality of TAM are significant and most components of Perceived Interactivity are significant. However the path Connectedness to Perceived Ease of Use and User Control to Perceived Playfulness is not significant. Among components of Perceived Interactivity, Personalization is the strongest antecedent of TAM. Perceived Usefulness is the strongest antecedent of behavioral intention of e-learning site.

• The Korean Journal of Community Living Science
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• v.17 no.2
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• pp.113-124
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• 2006

This study examines the factors influencing social capital within a lifelong learning city as discussed in the Youngcheon and Andong City case study. Data was collected from 205 leaners in Andong City and 194 learners in Youngchun City. The data was then analyzed by the SPSS WIN 12.0 program. The statistical techniques used for this study were frequency, percentile, 1-test, and regression. The major findings of this study were as follows: To begin with, the learners in LLC were more likely to have positive attitudes toward the construction of a lifelong learning city. Second, the leaners in LLC were more likely to have social capital including norm and network. Furthermore, the learning opportunity had a positive effect in increasing the trust between learners. Learning support and support systems also had influence in increasing the social network between learners. Finally, support systems had a positive effect in improving the norm between learners.