This study was based on Chikszentmihalyi's Flow theory, which classifies the distribution of various emotions (8 types), including flow, through various channels. The study sought to quantify and visualize the variables to understand different levels ...
This study was based on Chikszentmihalyi's Flow theory, which classifies the distribution of various emotions (8 types), including flow, through various channels. The study sought to quantify and visualize the variables to understand different levels of flow and other emotions. Through this, we tried to develop and expand the existing flow theory.
To fulfill the purpose of the study, we chose marine sports, which are challenging and immediate feedback sports that are easy to experience flow. The total number of samples in the Survey was 813, with 392 scuba divers and 421 surfers. As measurement variables, demographic characteristics were examined by gender, age, education level, marital status, and monthly income, and the main variables were Flow level, Feeling state, challenges level, skills level. As a data processing method, frequency analysis, descriptive statistics analysis, reliability analysis, K-means cluster analysis, and one-way analysis of variance were conducted using SPSS 27.0 version. In addition, two-dimensional and three-dimensional graphs in Excel Version 2309 of MOS were used to express the distribution and degree of feelings by level of variables. The significance level for all statistics was set to 5%.
Based on our analysis of the data, here are our findings. First, for both cluster and level of detail distributions, higher skills and challenges levels are associated with higher levels of flow. Second, for both cluster and level of detail distributions, higher skills and challenges levels are associated with higher levels of flow in the high skills and high challenges matching sectors. Third, the distribution of emotions does not appear to be steady or consistent across clusters or levels of granularity. Fourth, the level of flow and the distribution of flow emotions do not match to flow 8-channel theory.
The results of this study were inconsistent with existing theories of flow. A total of 9 clusters(9 channels) were identified through K-means cluster analysis according to the level of skills and challenges, showing a different channel form from the existing 8 flow channels. The level of flow was higher when the skill level was high or the challenge level was high. Regardless of whether the challenge level and skill level were matched or not. The level of flow was not simply a function of the level of skills and challenges. This is inconsistent with the traditional three-channel theory, where engagement occurs across the skills-challenges level matching band. It is also inconsistent with the traditional eight-channel theory, which suggests that engagement occurs in the high-skills, high-challenges level match area. And the distribution of flow emotions was partially consistent with the three-channel theory of flow, but not consistently, and not at all consistent with the eight-channel theory. As the existing flow theories are mainly based on universal everyday situations, it is necessary to distinguish the channels of flow and emotion through studies based on specific sports or situations, as in this study, and to check the distribution and level. This suggests that emotions, including flow, are difficult to explain only with two factors: skills and challenges. This study is meaningful in that it verifies the existing flow theory and attempted to theoretically expand it from a new perspective. Therefore, it will contribute to the need for additional variables to explain flow and emotions, and to expand the concept of the existing flow channel.