Analysis of Factors Influencing Happiness Level Using the MLP Neural Network Analysis Method: The Case of Korea

The aim of this study is to analyse the factors affecting the overall happiness level of Korean residents using neural network analysis, and to identify the resultant theoretical and policy implications. The analysis data for this study consist of survey data collected in 2020 by the Community Well-Being Research Center at Seoul National University Graduate School of Public Administration in Korea; the number of respondents was 16,555. The study employed the Multi-Layer Perceptron (MLP) method, one of the neural network analysis methods. It was found that the most important factor affecting the happiness of Korean residents was their own level of awareness of their social status. The next important was satisfaction with the level of infrastructure relating to their local living environment, the third most important being their level of awareness of their own income level. The study strongly suggests that awareness of the social status of local residents should be reflected in future happiness research and the establishment of happiness-related policies. It also suggests the need to apply new research methods, including neural network analysis, given that big data analysis has proven insufficient in happiness research. In the future, thanks to the development of artificial intelligence, the use of big data analysis methods will become more desirable in providing programmes for improving happiness levels tailored to individual situations.