• The Journal of Industrial Distribution & Business
• /
• v.9 no.2
• /
• pp.65-72
• /
• 2018

Purpose - One of the biggest problems in the e-learning distribution process is the lack of quality content and learners' discredit in e-learning content. In order to respond to the various demands of the corporate education field appropriately, it is necessary to search for directions of new e-learning models that are out of traditional e-learning contents. The purpose of this study is to identify recent trend issues related to corporate e-learning and to suggest directions for development. Research design, data, and methodology - Based on the literature review, trend issues that should be considered important in corporate e-learning were derived. Online survey was conducted to evaluate the importance-feasibility of each issue to 13 experts on e-learning and corporate education. The contents of the questionnaire are as follows: 1) recognition of importance and feasibility of trend issues to be considered important in the future corporate education field; 2) factors to be considered in developing future e-learning contents. Results - Six trends derived from a comprehensive literature review. The most important e-learning trends for corporate education field were 'mobile learning', 'micro learning', 'blended learning', 'social learning', 'adaptive learning', 'engaged learning'. As a result of evaluating the importance and feasibility of each issue, experts point out that 'mobile learning' and 'micro learning' should be actively considered for introduction and utilization at present. In addition, 'social learning' and 'blended learning' need to be actively considered in the near future. On the other hand, experts recognized that 'adaptive learning' and 'engaged learning' need to be prepared from a long-term perspective. Conclusions - There are two main reasons for this result. First, in corporate e-learning, it is important to 1) be able to update on time, 2) the connection with the workplace is important. Second, it requires realistic verification of the expected performance of the learning model. To be considered part of the future are as follows: First, the value and effectiveness of the new e-learning type should be studied. Seconds, e-learning contents should be developed through adopting SAM or Agile methodology. Through this process, we would be able to enhance the quality in e-learning content.

• Journal of vocational education research
• /
• v.29 no.3
• /
• pp.133-156
• /
• 2010

This study examines corporate support in learning on individual participation in learning and organizational learning. For this purpose, First, what does corporate support in learning affect individual participation in learning? Second, what does corporate support in learning and individual participation influence organizational learning? This study analyzed 1,268 employees at 139 companies. Sample sizes averaged about 9.22 employee per corporate. This paper used statistical method of hierarchical linear model. Above all, the findings show that corporate support in both formal and informal learning has meaningful effect on individual participation in formal learning and relationship. The findings reveal that corporate support in formal learning has influence on capacity, organizational memory, learning competency, adaptation to environment except sharing value. Furthermore, individual participation in learning has positive effect of increased organizational learning in all areas. In particular, it is shown that participation in informal relationship plays an important role to improve individuals' organizational learning ability.

• Educational Technology International
• /
• v.7 no.2
• /
• pp.1-21
• /
• 2006

Blended learning has become one of the major trends in Korean corporate training. However, there has been scant research on blended learning in corporate trainings settings in Korea. The purpose of the present study was to explore current and future trends of blended learning in corporate and other training settings in Korea. 136 people from training related fields such as human resource development (HRD), training, and e-learning participated in this research. The findings revealed many interesting current trends and future expectations related to blended learning in training settings. In regards to the overall status of blended learning in Korean corporations, participants displayed strong interest in blended learning and were expecting that the importance of blended learning would grow in their organizations either modestly or significantly during the next few years. In addition, the perceptions of the respondents regarding the benefits of blended learning and the barriers to implementation in their respective organizations were analyzed.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.1
• /
• pp.125-138
• /
• 2022

Companies want organizational members who take e-learning courses to enjoy the advantages of transcending time and space that e-learning has, but also want what they have learned to help the organization, the work they perform, or their future careers. In addition, while enjoying the effect of reducing education costs compared to offline education through e-learning, it is expected that executives and employees will apply the knowledge and skills learned to the field and perform tasks to achieve results. As COVID-19 continues, many education programs that have been conducted offline at corporate sites have been converted to e-learning, with a larger number of e-learning operations than in the past. This study was conducted based on the perception that learners' learning satisfaction is important for the successful operation of e-learning education, and that learners' own self-directed learning ability and self-determination are important as well as corporate efforts. As a result of the study, hypotheses 1-1, 1-2, 1-3-1, and 1-3-2 that the better the self-determination (autonomy, competence, full-time support, and peer support) is, the higher the learning satisfaction will be. Both Hypothesis 2-1 and Hypothesis 2-2 were adopted that the better self-directed learning (subjectivity, execution ability) is, the higher the learning satisfaction will increase. In conclusion, it is necessary to properly introduce the concepts of self-determination and self-directed learning in corporate education while operating with the corporate education system.

• Asia-Pacific Journal of Business
• /
• v.12 no.4
• /
• pp.13-40
• /
• 2021

Purpose - The purpose of this study is to predict corporate corruption in emerging markets such as Brazil, Russia, India, and China (BRIC) using different machine learning techniques. Since corruption is a significant problem that can affect corporate performance, particularly in emerging markets, it is important to correctly identify whether a company engages in corrupt practices. Design/methodology/approach - In order to address the research question, we employ predictive analytic techniques (machine learning methods). Using the World Bank Enterprise Survey Data, this study evaluates various predictive models generated by seven supervised learning algorithms: k-Nearest Neighbour (k-NN), Naïve Bayes (NB), Decision Tree (DT), Decision Rules (DR), Logistic Regression (LR), Support Vector Machines (SVM), and Artificial Neural Network (ANN). Findings - We find that DT, DR, SVM and ANN create highly accurate models (over 90% of accuracy). Among various factors, firm age is the most significant, while several other determinants such as source of working capital, top manager experience, and the number of permanent full-time employees also contribute to company corruption. Research implications or Originality - This research successfully demonstrates how machine learning can be applied to predict corporate corruption and also identifies the major causes of corporate corruption.

• Asia pacific journal of information systems
• /
• v.15 no.4
• /
• pp.27-60
• /
• 2005

The purpose of this study is to call attention to the consensus of stakeholders of corporate e-Learning system for the sake of its success. We identified the critical success factors(contents, technical features, management, and organizational support) as major components of corporate e-Learning systems and questioned whether stakeholders' consensus on the importance of these components facilitates the successful implementation of these components. We also questioned whether the influence of these components on user satisfaction could be moderated by contextual factors. Based on empirical testing of 18 e-Learning user companies, we verified that the consensus of stakeholders regarding the importance of content, technological features, and organizational support has a positive influence on the perceived quality of these factors in their e-Learning systems, which in turn is positively related to user satisfaction. The learning subjects and learning style did significantly moderate the influences of these perceived qualities on user satisfaction.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.267-276
• /
• 2021

This study discovered the effects of strategic learning and network building on a company's environmental management performance. According to the results, the environmental awareness of the company and competition threats within the industry did not significantly affect the establishment of environmental strategy, whereas the consumer's sensitivity to the environment and the environmental regulation of the government did. The environmental awareness of the company and the consumer's sensitivity to the environment were found to greatly impact a company's network building. which is closely related with the utilization of multimedia system and technology. In addition, it was found that the establishment of corporate environmental strategy had a significant effect on network building and strategic learning, but network building did not significantly affect strategic learning, indicating a difference. Finally, corporate strategic learning affected environmental management performance, suggesting an importance in accumulating strategic learning capabilities to increase environmental management performance.

• Knowledge Management Research
• /
• v.10 no.4
• /
• pp.31-42
• /
• 2009

With the rapid development of information technology, e-learning is growing in corporate. However, there are still problems in learning, such as low learning persistence rate. Learning outcomes are complex phenomenon influenced by a multitude of factors, it is need to considering the direct and indirect causal relationship among various factors. Therefore, the purpose of this study was to develop the causal model that explains the learning outcomes (satisfaction learning persistence) in corporate e-learning. This study was also intended to examine the causal relationship between the interaction and learning persistence through satisfaction mediators. For this, online survey was conducted with a sample of 270 learners who enrolled in cyber training course at A company. The major findings of this study are as follows: First, internality (internal locus of control, ${\beta}=.154$), interaction (${\beta}=.489$), satisfaction (${\beta}=.304$) have direct effect on learning persistence. Second, the interaction has direct effect on the satisfaction (${\beta}=.320$). Third, the satisfaction has direct effect on the learning persistence, and mediating the interaction and learning persistence. This result will contribute to build a learning strategy to improve learning outcomes.

• The Journal of Information Systems
• /
• v.29 no.1
• /
• pp.241-265
• /
• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.1-32
• /
• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.