This study aims to explore the factors influencing people’s intention to use home fitness mobile apps in the post-pandemic era. By incorporating the perspective of playfulness into the decomposed theory of planned behavior, it seeks to construct a behavioral model for the public's use of AR sports games for home exercise. The research focuses on Active Arcade users residing in Taiwan, employing the snowball sampling method to conduct an online questionnaire survey. A total of 340 valid questionnaires were collected and analyzed using linear structural equations. The study reveals three main findings: first, the behavioral model for Active Arcade users constructed based on the decomposed theory of planned behavior demonstrates a good fit; second, users’ attitudes, subjective norms, and perceived behavioral control have a positive and significant impact on behavioral intention; third, perceived usefulness, perceived ease of use, and perceived playfulness all positively and significantly influence attitudes, with perceived playfulness having the highest impact coefficient; fourth, perceived benefits of exercise are the most crucial factor affecting subjective norms; and fifth, convenience technologies are the key factor influencing perceived behavioral control. This study provides valuable insights for theory and management practice, offering guidance on the use of home fitness apps in the post-pandemic era while addressing research limitations and suggesting future directions.

The purpose of this study is to identify the effects of multidimensional (fuzzy) inequalities and marginal changes on the Gini coefficients of various factors. This allows a range of social policies to be specifically targeted to reduce broader inequalities, but these policies are focused primarily on health, education, housing, sanitation, energy and drinking water. It is necessary to target policy areas that are unequally distributed, such as those with access to unevenly distributed drinking water policies. The data are from the Household and Consumption Survey of 6695 households in 2003 and 9259 households in 2011. This paper uses Lerman and Yitzhaki’s method. The results revealed that the main contributors to inequalities over the two periods were health and education. These sources have a potentially significant effect on total inequality. Health increases overall inequalities, but sources such as housing, sanitation and energy reduce them. This article provides resources to disadvantaged and vulnerable target groups. Multiple inequalities are analyzed for different subgroups of households, such as place of residence and the gender of the head of household. Analyzing fuzzy poverty inequalities makes it possible to develop targeted measures to combat poverty and inequality. This study is the first to investigate the sources of Gini’s fuzzy inequality in Chad via data analysis techniques, and in general, it is one of the few studies in Saharan Africa to be interested in this subject. Some development policies in sub-Saharan Africa should therefore focus on different sources (negative effect), sources (positive effect) and the equalization effect.

The smallest administrative unit of the sixth national census-township (town) is selected as the basic unit, the population spatial distribution characteristics at the township (town) level in karst mountainous areas of northwest Guangxi are analyzed by using Lorenz curve and spatial correlation analysis method, and the influence intensity of natural factors on regional population spatial distribution is detected by using geographic detector method. The results show that: 1. the spatial distribution of population at the township (town) level has the characteristics of imbalance, showing generally significant positive correlation and certain aggregation; 2. There are significant differences in the impact of the spatial distribution of various natural factors on the population distribution. For the towns without karst distribution in the northwest and central south of the study area, the population density increases with the increase of factors conducive to human residence, but the average population density is only 79 people/km². In the towns with karst distribution in the East and south, the spatial distribution of population density and natural factors is not a simple increase or decrease relationship, but fluctuates with the change of karst distribution area. 3. The factor detection results of the geographic detector show that the altitude has the greatest impact on the spatial distribution of population. The interactive detection results show that the impact intensity of any two natural factors after superposition and interaction presents nonlinear enhancement and two factor enhancement. It can be seen that the karst mountain area in northwest Guangxi is similar to other areas. Altitude is one of the main factors affecting the spatial distribution of population, but the river network density and unique geological landform of karst mountain area have a strong catalytic effect on the spatial distribution of population. The superposition and interaction with other factors can further strengthen the impact on population distribution.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.