PURPOSE This study aims to analyze the competitive advantages of Japanese and South Korean national football teams using the value, rarity, imitability, and organization (VRIO) model. Based on the results, it proposes strategies for the development of South Korean football. METHODS The research methodology is a combination of literature review, case study, and semi-structured interviews with football experts. RESULTS The Japanese football system meets all criteria of the VRIO model through its systematic youth development system, data-driven strategies, and organizational linkage between clubs and the national team, which has led to consistent performance in international tournaments. In contrast, while South Korean football possesses excellent individual player resources, it fails to fully meet the criteria of the VRIO due to a regional imbalance in the youth system, insufficient use of data, and lack of cooperation between clubs and the national team. CONCLUSIONS The interview data indicate that strengthening the youth system, adopting a data-driven approach, and improving the collaborative structure between clubs and the national team are necessary components of the development of South Korean football. This study provides specific directions for the long-term and sustainable development of South Korean football by analyzing the strategic competitiveness of national football teams in other countries using the VRIO model.
PURPOSE The purpose of this study is to find the best model to predict the demand of visitors in Let’s Run Park by using machine learning and to provide effective data for establishing future marketing strategies. METHODS For this purpose, three methods of machine learning were applied: random forest, adaboost, and gradient boosting. The variables for predicting the audience were weather data and the number of visitors per date for four years as training data, and the accuracy was predicted by comparing the actual data for one year. RESULTS First, the performance evaluation using random forest was conducted, RMSE =1856.067, R2= .965, and error was 6.47%. Second, the performance evaluation using Adaboost was conducted, RMSE =1836.227, R2= .965, and error was 5.25%, which was the lowest among the three machine learnings. Third, the performance evaluation using gradient boosting showed that RMSE =1797.400 and R2= .967 were the most accurate among the three machine learnings and error was 6.99%. CONCLUSIONS As a result of this study, each of the three machine learning features existed, but the most efficient model was gradient boosting. In addition, the best way to utilize it in the field is to predict the number of visitors by comprehensively judging the results of the three machine learning, and it is judged that it will help efficient management decision making in the future.
