• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.127-139
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• 2024

This study investigates the impact of edutech characteristics and both tangible and intangible educational services on the intention to re-enroll, which is directly related to the management performance of private institutes. The study aims to propose strategies to improve re-enrollment intentions and management performance based on the findings. Private education has grown continuously, complementing the limitations of public education and increasing parental dependence. This study tested the hypothesis that edutech characteristics, intangible services, and tangible services, increasingly utilized with the development of information and communication technology, would influence re-enrollment intentions. It also examined whether rapport-building behavior with parents would have a moderating effect on this relationship. The hypothesis testing results showed that among the edutech characteristics, content, intangible services such as reliability and empathy, and tangible services such as tangibility and payment accessibility positively impacted re-enrollment intentions. The hypothesis that rapport-building behavior would moderate the relationship between educational services and re-enrollment intentions was supported for empathy in intangible services and tangibility in tangible services. Based on these findings, the study proposed three strategies to improve management performance of private institutes. First, in terms of improving and managing edutech characteristics, it suggested introducing and updating edutech content and ensuring operational stability. Second, for improving and managing intangible services, it recommended managing instructor recruitment and training to enhance quality and competence, maintaining professionalism through continuous education by credible institutions, and providing level-based education for students based on the qualitative improvement of educational programs. Third, to improve and manage tangible services, it suggested setting appropriate tuition fees, offering various payment methods (online, mobile, card, bank transfer) unrestricted by time and place, and equipping interiors and facilities that enable focused learning. Additionally, considering the moderating effect of rapport-building behavior, it emphasized that improvements and management requiring costs are necessary, but making parents feel a high level of tangibility through rapport-building is also important. Furthermore, given the increasing importance of edutech based on information and communication technology, the study highlighted the need for various support measures such as government technological support and venture certification system support for institutes with an entrepreneurial spirit aiming to introduce innovative technologies such as AI technology based on large language models and AR/VR-applied metaverse environments. This study is expected to help improve the management performance of private institutes by specifically suggesting items and methods for improvement and management in the educational field.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.133-144
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• 2022

This study was conducted to investigate the cultivation period, adaptive genetic resources, growth and development patterns, and water consumption for rice cultivation in the desert environment of United Arab Emirates (UAE). R esearch on rice cultivation in the desert environment is expected to contribute to resolving food shortages caused by climate change and water scarcity. It was found that the optimal cultivation period of rice was from late November to late April of the following year during which the low temperature occurred at the vegetative growth stage of rice in the UAE. Asemi and FL478 were selected to be candidate cultivars for temperature and day-length conditions in the desert areas as a result of pre-testing genetic resources under reclaimed soil and artificial meteorological conditions. In the desert environment in the UAE, FL478 died before harvest due to the etiolation and poor growth in the early stage of growth. In contrast, Asemi overcame the etiolation in the early stage of growth, which allowed for harvest. The vegetative growth phases of Asemi were from early December to early March of the following year whereas its reproductive growth and ripening phases were from early March to late March and from late March to late April, respectively. The yield of milled rice for Asemi was 763kg/10a in the UAE, which was about 41.8% higher than that in Korea. Such an outcome was likely due to the abundant solar radiation during the reproductive growth and grain filling periods. On the other hand, water consumption during the cultivation period in the UAE was 2,619 ton/10a, which was about three times higher than that in Korea. These results suggest that irrigation technology and development of cultivation methods would be needed to minimize water consumption, which would make it economically viable to grow rice in the UAE. In addition, select on of genetic resources for the UAE desert environments such as minimum etiolation in the early stages of growth would be merited further studies, which would promote stable rice cultivation in the arid conditions.