• 통상정보연구
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• 제12권3호
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• pp.311-332
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• 2010

This study investigate the effects of environmental and organizational characteristics on e-business competence and the influence of e-business competence on export performance in the small and medium sized firms. The development of the research model is based on the literature of e-business and the empirical studies of information technology competence. The data from the survey was analyzed using Partial Least Squares(PLS). The results from the empirical model suggest that e-business competence is affected by environmental uncertainty and market diversity as well as top management support and learning orientation. And, export performance is enhanced by e-business competence.

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.93-99
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• 2022

Nowadays, learning management systems (LMS) are an effective and efficient tool for providing students with a high-quality education. The current study examines the effect of different factors on the use of the blackboard application on mobile phones. The study selects four important factors after factor analysis, such as facilitating factor, performance factor, satisfaction factor, and difficulties factor. The data was collected through a structured questionnaire from 45 students as a sample in the college of business administration at Prince Sattam Bin Abdulaziz University. The study uses a logistic regression model to examine the empirical relationship between LMS adoption and different factors associated with blackboard adoption. The results show that 71 percent of the respondents are between the age of 18-20 years, and 100 percent of students have experience in using blackboard. The empirical results show that the satisfaction factor is positive and significant at the 10 percent level of significance and the difficulties factor is also positive and significant at the 1 percent level of significance. The results conclude that the students are satisfied with using the blackboard on mobile, nevertheless, the difficulties factor which is positive and significant shows that students are facing some difficulties in using the blackboard on their mobile.

• The Journal of Asian Finance, Economics and Business
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• 제8권10호
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• pp.385-393
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• 2021

In an effort to find ways to increase the effectiveness of online education, literature and empirical study based on the Technology Acceptance Model (TAM) have addressed a variety of questions, including perceived ease of use (PEU) and perceived usefulness (PU). After TAM, extensive studies have focused on the impact of extrinsic factors on PEU and PU, including Self-efficacy and Perceived Risk. This study aims to analyze the direct, indirect, and moderating effects of Self-efficacy and Perceived Risk on Online Learning Intention (OLI). Data was collected through a survey method from 472 students studying at universities in Vietnam. The collected data was analyzed using the PLS-SEM technique to test the hypotheses. The findings reveal that Technology Self-Efficacy influences the intention to take online courses both directly and indirectly through Perceived Ease of Use and Perceived Usefulness. Besides, Perceived Risk COVID-19 also has a positive effect on online learning intention, and plays a role as a moderating variable on the impact of PU on OLI. These findings suggest that students will have a stronger intention to study online when they are confident in their ability to use technology. When they believe in their ability to use technology, their online learning intention will also increase.

• 한국경영과학회지
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• 제39권4호
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• pp.115-136
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• 2014

The game industry has grown steadily and the online game has become one of the most attractive game segments for its remarkable growth. Customer management in the online game industry, however, has received little attention from the academic field. The purpose of this study is to analyze the drivers of customer defection in the online game setting and suggest not only theoretical but also managerial insights into increasing customer retention rates. Prior to empirical analysis, the authors hypothesized that 3 variables of interests (Learning, Playing, Achievement) would explain the customer defection according to preceeding researches. To demonstrate these hypotheses, the authors obtained data from one of the biggest game publishers in Korea, and the empirical analysis model was developed considering context of research settings. The results of analyses provide the following insights. First, the key behavioral variables of Learning, Playing, and Achievement play substantial roles in explaining the customer defection. Next, the effects of these variables vary between customer types: novice and experienced customers. The defection decisions by novice customers are predicted by all key behavioral variables and Playing serves as the most influential indicator of the defection decisions. However, experienced customers are influenced by Playing and Achievement, while Learning has no impact on the defection decisions. Finally, the authors investigated hypothetical customer retention strategies, using the empirical results. The market outcomes indicate that the customer retention strategies work well with novice customers and it is hard-to-impossible to prevent experienced customers from defection using their behavioral data. These findings together deliver several meaningful insights to management as follow. First, the management should support customers to get involved in Learning activities at the very first stage. Second, customer's Achievement and appropriate compensation for it would work as defection barriers. Last, to optimize the outcomes of firm's marketing investments, it is better to focus on retention of novice users not experienced ones.

• Journal of Computing Science and Engineering
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• 제5권2호
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• pp.150-160
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• 2011

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

• 지식경영연구
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• 제6권2호
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• pp.67-90
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• 2005

Attention has been focused recently on the concept of shared cognition which encompasses the notion that effective team members hold knowledge that is overlapping and complementary with teammates. This shared cognition is expected to improve team effectiveness. In contrast to the continued efforts in developing theoretical approach of shared cognition, empirical studies are meager. Thus, we conducted an empirical study to investigate the role of shared cognition on team effectiveness. This study classifies shared cognition into two types, team mental model and transactive memory system, by shared meaning. A total of 121 new product development teams in the IT industry were surveyed for the data collection. The results of analysis can be summarized as follows: first, team mental model has a positive influence on team performance, team innovative behavior and team learning effect. And the relation with team mental model and team performance is moderated by the similarity of knowledge structure among the expert. Second, transactive memory system has a positive influence on team performance, team innovative behavior and team learning effect.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• 디지털융복합연구
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• 제6권2호
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• pp.135-143
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• 2008

The remarkable growth of the Internet since mid-l990s has expanded the e-learning market and brought the transformation of educational environments and methodology. It can be said that the e-learning has changed the educational paradigm. Korean government is firmly determined to support the diffusion of e-learning because of the benefits of e-learning. People seem to accept the e-learning when its contents have high quality. A lot of research have been conducted on e-learning, however, it was mostly about user's usage intention, satisfaction and educational effect. It can't seem that sufficient research efforts have been put into figuring out the role of e-learning contents quality in the expansion of e-learning. In this paper, we present the empirical study on the influence of e-learning contents quality on user's satisfaction and educational effect. We conducted an questionnaire survey on college students to collect data and found that the quality of e-learning contents has significant influence on the users' satisfaction.

• 한국해안·해양공학회논문집
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• 제36권2호
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• pp.37-49
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• 2024

지금까지 쇄파는 발생기구의 본질적인 복잡성으로 인해 실내수리모형실험을 통해 쇄파파고 및 쇄파수심 등의 쇄파지표 예측을 위한 많은 경험식이 제안되어 왔다. 하지만, 자갈해빈에 대한 쇄파의 특성 및 쇄파지표예측을 위한 연구는 거의 수행되어 있지 않았다. 본 연구에서는 자갈해빈을 대상으로 쇄파파고 및 쇄파수심의 예측을 위하여 회귀 또는 분류 문제와 관련된 다양한 연구 분야에서 높은 예측 성능을 보이는 대표적인 선형기반 기계학습기법에 기반한 쇄파지표를 예측하고자 하였다. 먼저, 자갈해빈에 대하여 기존에 제안된 쇄파지표의 경험식의 적용성을 검토하고 기존의 경험식의 자갈해빈의 쇄파지표 예측성능의 한계성을 극복하기 위하여 다양한 선형기반 기계학습 알고리즘을 적용하여 쇄파지표 예측모델을 구축하였다. 구축된 기계학습모델 중 자갈해빈에서 발생하는 쇄파파고 및 쇄파수심에 대한 높은 예측성능을 보인 모델을 기반으로 손쉬운 계산이 가능한 쇄파지표에 대한 새로운 산정식을 제안하였고 수리모형실험결과 및 기존의 경험식과 비교하고 새롭게 제안한 쇄파지표의 예측성능을 검증하였다. 본 연구에서 제안한 쇄파지표에 대한 경험식은 단순한 다항식임에도 불구하고 자갈해빈에 대한 양호한 예측성능을 보였다.

• 대한수학교육학회지:수학교육학연구
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• 제9권2호
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• pp.419-438
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• 1999

This paper explored the impact of dynamic geometry software such as CabriII, GSP on student's understanding deductive justification, on the assumption that proof in school mathematics should be used in the broader, psychological sense of justification rather than in the narrow sense of deductive, formal proof. The following results have been drawn: Dynamic geometry provided positive impact on interacting between empirical justification and deductive justification, especially on understanding the necessity of deductive justification. And teacher in the computer environment played crucial role in reducing on difficulties in connecting empirical justification to deductive justification. At the beginning of the research, however, it was not the case. However, once students got intocul-de-sac in empirical justification and understood the need of deductive justification, they tried to justify deductively. Compared with current paper-and-pencil environment that many students fail to learn the basic knowledge on proof, dynamic geometry software will give more positive ffect for learning. Dynamic geometry software may promote interaction between empirical justification and edeductive justification and give a feedback to students about results of their own actions. At present, there is some very helpful computer software. However the presence of good dynamic geometry software can not be the solution in itself. Since learning on proof is a function of various factors such as curriculum organization, evaluation method, the role of teacher and student. Most of all, the meaning of proof need to be reconceptualized in the future research.