• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.431-443
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• 2004

Many e-Learning companies are incorporated for the last five years, but most of them are failed or merged by the other company. The main reasons are the absence of competitive strategies and recognition of critical success factors. There are many researches on the critical success factors of Information System (IS) and Electronic Commerce (EC) . We derived e-Learning success factors from the previous IS and EC researches. We classified the success factors into five dimensions, (1) contents management, (2) learner management, (3) business strategy, (4) organizational support and ability, (5) learning management system (LMS), and each dimension has 9 or more success factors measurement items. We surveyed the perceived importance of the success factors from the manager of South Korea e-Learning firms. The paper categorized the items into two or more factors for each dimension by the exploratory factor analysis. Finally, we conducted one-way ANOVA for each success factors by the business model. As a result, there is different importance level for each success factors by the business model. We concluded that each e-Learning company needs different strategies to their business model.

• Knowledge Management Research
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• v.15 no.2
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• pp.67-88
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• 2014

This study aims to investigate on measuring the success of e-Learning. For this purpose, we proposed a research model that consists of e-Learning contents quality, e-Learning system quality, e-Lecturing quality, sense of e-Learning community factors as independent factors and e-Learning and e-Learning satisfaction as mediators and tested it empirically based on the structural equation model. The empirical results showed that e-Learning contents quality, e-Learning system quality, sense of e-Learning community factors directly lead to e-Learning. The study also found that e-Learning contents quality, e-Lecturing quality, sense of e-Learning community factors bring about higher e-Learning satisfaction and that e-Learning satisfaction has a positive impact on e-Learning. Furthermore, the research discovered that both e-Learning and e-Learning satisfaction have a significant relationship with e-Learning net benefits. This research renders its theoretical contribution to analyzing a positive influence of sense of e-Learning community, a newly suggested variable added to the existing IS success model in this study, on e-Learning. From a practical view, the findings of this study can lead to improving the quality of e-Learning in today's era where the growth of e-Learning industry is quite noticeable.

• The Journal of Information Systems
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• v.15 no.4
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• pp.171-188
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• 2006

Information technology and the Internet have had a dramatic effect on education method and individual life. Universities and companies we making large investments in e-Learning applications but are hard to pressed to evaluate the success of their e-Learning systems. e-Learning can be seen as not only one of Internet based information systems which can provide education services but also one of teaching-teaming methods which can implement self-directed teaming. This paper tests the updated model of information system success proposed by Delone and McLean using a field study of a e-Learning. The five dimensions - information quality, system quality, service quality, user satisfaction, net benefit - of the updated model are parsimonious framework for organizing the e-learning success metrics identified in the literature. Questionaires are collected from 107 students who are enrolling a e-learning class using online survey. The model is tested using SPSS and LISREL. The results show that information quality and service quality are significant predictors of user satisfaction with the e-Learning system but system quality is not. Also user satisfaction is found to be a strong predictor of the learning performance. This strong association between user satisfaction and teaming performance suggests that user satisfaction may serve as a valid surrogate for teaming performance. Empirical testing of the updated DeLone & McLean model should therefore be extended to cover a wider variety of systems.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.77-88
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• 2022

In response to the significant COVID-19 outbreak, countries have enforced the use of E-learning systems as an alternative to traditional learning; to contain the virus and minimize the infection rate while maintaining the continuity of the learning experience. However, the effective adoption of E-learning systems requires a well-understanding of critical factors, especially in times of crisis. In this regard, this study intends to assess the success of the E-learning system adoption by Higher Education Institutions (HEIs) during the crisis of COVID-19 by utilizing the Information Systems Success (ISS) model. This study's adopted model consists of nine interdependent dimensions, namely: Technical System Quality, Information Quality, Service Quality, Learner Quality, Perceived Satisfaction, Perceived Usefulness, System Use, Intention to Use, and System Success. An electronic survey was distributed among higher education students from different universities in Saudi Arabia to explore each model's dimension. Structural Equation Modeling (SEM) has been applied via SmartPLS software to test the causal relationships between dimensions. This study's main results revealed that students' Service Quality, Learner Quality, and the Intention to Use by students are essential drives for E-learning System Use during the Covid-19 pandemic. Meanwhile, the Intention to Use the system is significantly influenced by Perceived Satisfaction and Perceived Usefulness dimensions. Further, Perceived Satisfaction, Perceived Usefulness, and System Use are interdependent, and all three have a significant positive impact on E-learning System Success.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.831-843
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• 2020

The objective of this study is to examine the effects of audit socialization and professional commitment on professional success of tax auditors in Thailand through individual learning as the moderator. The specific research questions are: (1) How audit socialization affects professional commitment, (2) How professional commitment influences professional success, and (3) How individual learning moderates the audit socialization-professional commitment relationships, the audit socialization-professional success relationships, and the professional commitment-professional success relationships. This study collected data from 249 tax auditors in Thailand by using questionnaire. To investigate the research relationships, both structural equation model and multiple regression analysis are implemented. Within the research results, audit socialization has a significant positive effect on professional commitment and professional success while professional commitment has an important positive influence on professional success. Similarly, individual learning positively moderates the professional commitment-professional success relationships. In summary, audit socialization is important for auditing professions and it is a key determinant of professional success. Thus, auditors need to pay attention to audit socialization through learning and understanding it and applying its concepts to audit works to increase auditors' professional success, continuous survival and long-term sustainability.

• Asia pacific journal of information systems
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• v.16 no.1
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• pp.1-21
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• 2006

e-Learning can be seen as not only one of Internet-based information technologies which can provide education services but also one of teaching-learning methods which can implement self-directed learning. Thus, for evaluation of e-Learning effectiveness, both information-technology-based learning environment and learners' abilities in self-learning and computer-using should be considered simultaneously. This study suggests a research model for evaluating the effectiveness of e-Learning, which is theoretically based on information systems success model, constructivism and self-efficacy. The model is composed of three parts: effectiveness, learning environment, and learners' self-efficacy. Effectiveness is a part of dependent variables: satisfaction and academic performance. Learning environment and learners' self-efficacy can be considered as two sets of explanation variables for effectiveness. The former consists of learning management system, learning contents, and interactions that are provided bye-Learning and the latter means learners' self-regulated efficacy and computer self-efficacy. We show validity of the model empirically by surveying the college students who have experienced e-Learning. In result, most of all hypotheses suggested in this model are accepted in low significant level.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.103-110
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• 2020

The course evaluations was used to collect the students' opinion and verify the learning performance for a long time. However, many evaluations' methods are not consider each characteristics of the course, and many courses were presented to students with the same question for the course evaluations. This research aims to make a learning success model considering the questionnaire of course evaluations and relationship between the factors of learning and learning satisfaction/benefit. In order to make a model, we identify the items of the questionnaire and distinguish the factors considering the items. The factors are Information Quality, Knowledge Quality, System Quality and Service Quality. Consequently, the research shows the connection and relationship between them and learning satisfaction/benefit.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.