Research on Development of Sponsorship Effect Analysis Module Using Text Mining Technique

To expand the sponsorship market, sponsorship activities based on sponsorship effect analysis data through scientific and systematic analysis must be carried out. As the development of the media industry and the COVID-19 pandemic have caused many changes in the method of broadcasting professional sports, it is necessary to upgrade the analysis of sponsorship effects. In a crisis situation where the sponsorship effect analysis market is shrinking due to the COVID-19 pandemic, the development of brand exposure analysis programs that can be used based on online platforms will expand the sponsorship market and promote changes in the domestic analysis market that relies on overseas analysis programs. In this study, more than 200,000 online sports media data were collected to analyze the sponsorship effect by minimizing the omission of sponsor brands exposed through online media and detecting sophisticated data. A commercial dictionary in the sports field was established and a sponsorship effect analysis module was developed by quantifying text data using morpheme analysis and TF-IDF. A module based on UI was implemented to analyze the results of the custom morpheme analyzer and Elasticsearch Term Vectors AP. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

AI Recruitment System Using EEG to Explore the Truth of Interviewers

COVID-19 pandemic is behind the implementation of the "AI recruitment system.” The number of companies trying to introduce AI recruitment systems is increasing because the non-face-to-face method is recommended due to the COVID-19 pandemic and the management change of the organization comes with the development of IT technology. Behind the positive evaluation that the development of AI technology improves the efficiency of work, the demand for fair and transparent recruitment procedures has been increasing as controversy over fairness and objectivity has increased due to various hiring irregularities. This study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In the previous study, voice and video were identified based on ML. In situations where the problem of truth and falsehood is raised, this study conducted EEG-based biological experimental studies with a deep learning method to explore more objectively. Also, the experimental design applied biological experiments between brain activity patterns and brain regions as signals from EEG-based 14 channels to explore the truth/false authenticity of the experimenters. As a result of the experiment, the best performance and effect were shown in the CNN model with an accuracy of 91% truth and 89% false among the comparative analysis of Decision Tree, Random Forest, and CNN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

AI Recruitment System Using EEG to Explore the Truth of Interviewers

COVID-19 pandemic is behind the implementation of the “AI recruitment system.” The number of companies trying to introduce AI recruitment systems is increasing because the non-face-to-face method is recommended due to the COVID-19 pandemic and the management change of the organization comes with the development of IT technology. Behind the positive evaluation that the development of AI technology improves the efficiency of work, the demand for fair and transparent recruitment procedures has been increasing as controversy over fairness and objectivity has increased due to various hiring irregularities. This study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In the previous study, voice and video were identified based on ML. In situations where the problem of truth and falsehood is raised, this study conducted EEG-based biological experimental studies with a deep learning method to explore more objectively. Also, the experimental design applied biological experiments between brain activity patterns and brain regions as signals from EEG-based 14 channels to explore the truth/false authenticity of the experimenters. As a result of the experiment, the best performance and effect were shown in the CNN model with an accuracy of 91% truth and 89% false among the comparative analysis of Decision Tree, Random Forest, and CNN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Effect of Self-Determination and Quality of VR-Based Education in the Metaverse on Learner Satisfaction

While online learning had traditionally been implemented to aid in-person education, it has recently evolved into a critical tool for remote education. In particular, the advent of the COVID-19 pandemic has accelerated the spread of online learning and its significance. However, this wave of innovation in the education field has revealed the lack of research on how online education should be systematically provided and which educational aspects should be considered to enhance learner satisfaction in online settings. With the recent emergence of the Metaverse, VR education is once again proposed as a major tool for online learning despite existing limitations in research which put both systematic and educational aspects into consideration. Hence, this study presents and discusses a research model that converges technology acceptance model, information systems success model, and self-determination theory with the purpose of exploring variables that affect learner satisfaction, mediating the flow theory. Results showed that most variables of both self-determination theory and information system quality within VR education impact learner satisfaction. In particular, learners’ flow—complete immersion in learning—functions as an important mediating variable for learner satisfaction. These findings suggest designing and running a systematic platform that reflects self-directed learning is imperative to bring the best educational practices into the Metaverse. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Study on the Effect of Customer Usage on the Intention of Continuously Coffee Brand App based on UTAUT Model

Due to the spread of COVID-19, the use of mobile applications is increasing in coffee shops as activities that have been conducted face-to-face are being carried out non-face-to-face. The purpose of this paper is to make strategic suggestions to positively improve the continuous use intention of coffee shop mobile applications by checking the effect of the expanded integrated technology acceptance model and online review. An online survey was conducted on mobile and statistical analysis used the PLS structural equation model. As a result, it is expected that the satisfaction with the use of coffee shop applications and the influence of online reviews in customer experience will recognize positive values, affect the intention to continue using them © 2022 IEEE.

Metaverse Learning: The Relationship among Quality of VR-Based Education, Self-Determination, and Learner Satisfaction

While online learning had traditionally been implemented to aid in-person education, it has recently evolved into a critical tool for remote education. In particular, the advent of the COVID-19 pandemic has accelerated the spread of online learning and its significance. However, this wave of innovation in the education field has revealed the lack of research on how online education should be systematically provided and which educational aspects should be considered to enhance learner satisfaction in online settings. With the recent emergence of the Metaverse, VR education is once again proposed as a major tool for online learning despite existing limitations in research which put both systematic and educational aspects into consideration. Hence, this study presents and discusses a research model that converges technology acceptance model, information systems success model, and self-determination theory with the purpose of exploring variables that affect learner satisfaction, mediating the flow theory. Results showed that most variables of both self-determination theory and information system quality within VR education impact learner satisfaction. In particular, learners' flow-complete immersion in learning-functions as an important mediating variable for learner satisfaction. These findings suggest designing and running a systematic platform that reflects self-directed learning is imperative to bring the best educational practices into the Metaverse. © 2022 IEEE.

Factors Affecting the Intention to Use Artificial Intelligence-Based Recruitment System: A Structural Equation Modeling (SEM) Approach

The controversy over fairness and objectivity in the job market, due to hiring irregularities, has led to calls for transparent and fair recruitment procedures. Advances in IT technology have led to the emergence of a non-face-to-face “AI recruitment system” in which artificial intelligence (AI) conducts interviews, instead of human interviews. As the introduction of the non-face-to-face method is encouraged in the hiring process due to the COVID-19 virus pandemics, the number of companies introducing AI recruitment systems is steadily increasing. In this study, the factors affecting the intention of use of AI-based recruitment system were analyzed by utilizing TOE and TAM. As a result, it was shown that the reliability, security, suitability, new technology, partiality, readiness, and legal and policy environment of the TOE affected the intention of using the system. It was also identified to have the moderating effect of the number of employees in the firm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Prediction of COVID-19 Using LSTM Algorithms

As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socio-economic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data. © 2021 The Authors. Published by Atlantis Press B.V.

A Study on Factors Affecting the Intention to Use Library Information System

The new virus COVID-19 outbreak in Wuhan, China on Dec. 08, 2019 has had a huge impact on all sectors of society including economy, politics, and science sector, around the world. As a result, the government is continuing its contactless lifestyle by implementing life prevention guidelines which prevent the spread of COVID-19 by minimizing human-to-human contact. Several public user services are also being converted to non-contact way, changing the overall environment of our society. The Library Information System, one of the user services, provides various services through online and offline, including lending and reading books, non-books lending and reading, and operating cultural spaces. However, due to the influence of COVID-19, online user services are required to be expanded, making it important for the Library Information System to improve stability and reliability. This study defined the user service of the Library Information System and examined the variables that are needed for the construction of next generation Library Information System through a group of experts. Based on these selected factors, the research contributes to the construction of the next generation Library Information System with the correlation results of the empirical analysis. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.