The complementarity interference (CI) model suggests that the Internet may either inhibit or facilitate interpersonal communications. This paper empirically examines the impact of Internet usage on interpersonal interactions, using a micro dataset from China to answer whether the Internet brings people closer together or further apart. The empirical results demonstrate, first, that Internet usage significantly increases both the time and frequency of people’s communications with their family and friends, rather than causing them to feel more disconnected and isolated. Holding other factors constant, for each one-standard-deviation increase in Internet usage, weekly communications with family members increases by an average of 102.150 min, while there is an average increase of 54.838 min in interactions with friends. These findings as to its positive effects are robust when using other regression models and interpersonal contact measures, as well as the instrumental variable method. Second, Internet usage also contributes to decreased loneliness; it exerts this effect primarily by improving people’s interactions with their family members. However, communications with friends do not significantly mediate such impacts. Third, the positive role of Internet usage on communications is more prominent for people with more frequent online socialization and self-presentation, better online skills, younger age, higher educational level, and who are living in urban areas. In addition, the beneficial effects of Internet usage are larger for communications with family members in the case of migrants. Therefore, in the context of the rapid development of information technology, the network infrastructure should be improved to make better use of the Internet to facilitate interpersonal communications and promote people’s wellness.

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

This paper uses a large-scale nationally representative dataset, the Chinese General Social Survey, to examine the relationship between subjective well-being and depressive disorders. Statistical results indicate that higher levels of subjective well-being help decrease perceived depression. Robustness checks are carried out using different types of explanatory and dependent variables, various regression models, penalized machine learning methods, instrumental variable approaches, and placebo tests, all of which lend further credence to the above findings. Based on it, heterogeneities in the relationship between subjective well-being and self-rated mental disorders are explored. In respect of variations in age cohorts, it is found that the absolute values of happiness’s estimated coefficients are smaller in the 20–30 and 30–40 age groups, while that in the 40–50 age group increase substantially. In older cohorts, the estimates remain at higher levels while fluctuating to some degree. Furthermore, the significantly negative interaction between happiness and age proves that age amplifies subjective well-being’s effect on perceived depressive disorders. With age increasing, the impact of happiness on reducing perceived depression tends to be stronger. Therefore, for older people, subjective well-being plays a more important role in suppressing self-rated depression. Heterogeneities of the relationship between happiness and perceived depressive disorders in subgroups with different demographic characteristics are also investigated. It is found that the negative correlation between subjective well-being and self-rated depression is stronger among those with higher educational levels, living in urban areas, being members of the Communist Party of China, having pensions, and owning more housing assets. However, gender, ethnic identity, religious belief, and marital status exert no significant moderating effects.

Purpose: Existing studies on the association between BMI and depression report conflicting results with some demonstrating a positive relationship, while others a negative link or insignificant correlation. Very limited research on the nonlinear relationship between BMI and depression has yet to clarify the reliability and robustness of the potential nonlinearity and whether a more complex association exists. This paper aims to systematically investigate the nonlinear relationship between the two factors applying rigorous statistical methods, as well as explore the heterogeneity of their association. Materials and Methods: A large-scale nationally representative dataset, Chinese General Social Survey, is used to empirically analyze the nonlinear relationship between BMI and perceived depression. Various statistical tests are employed to check the robustness of the nonlinearity. Results: Results indicate that there is a U-shaped relationship between BMI and perceived depression, with the turning point (25.718) very close to while slightly larger than the upper limit of the range of healthy weight (18.500 ≤ BMI < 25.000) defined by World Health Organization. Both very high and low BMIs are associated with increased risk for depressive disorders. Furthermore, perceived depression is higher at almost all BMI levels among individuals who are older, female, lower educated, unmarried, in rural areas, belonging to ethnic minorities, non-Communist Party of China members, as well as those with lower income and uncovered by social security. In addition, these subgroups have smaller inflection points and their self-rated depression is more sensitive to BMI. Conclusion: This paper confirms a significant U-shaped trend in the association between BMI and depression. Therefore, it is important to account for the variations in this relationship across different BMI categories when using BMI to predict depression risk. Besides, this study clarifies the management goals for achieving an appropriate BMI from a mental health perspective and identifies vulnerable subgroups at higher risk of depression.

In the context of the “Chinese Differential Mode of Association” in traditional Chinese culture, this paper examines the heterogeneous effects of interpersonal interactions in different relationships on older adults’ depressive emotions from a cultural-psychological perspective. Results using data from Chinese General Social Survey demonstrate that: interactions with children are the most helpful in reducing perceived depression for the elderly, followed by communications with siblings and relatives. However, interactions with friends and other fellows do not significantly reduce older people’s perceived depression. This reflects the “Chinese Differential Mode of Association” in interpersonal relationships. When using different perceived depression measures, and Double Debiased Machine Learning (DDML) approaches for robustness and endogeneity tests, above findings are very robust. The impact mechanism is that interactions with children and siblings reduce depressive emotions by decreasing older adults’ sense of loneliness, while communications with others do not have such a significant effect. This paper further discusses the roles of different types of interactions with adult children. It is found that receiving and providing emotional support can prominently decrease depressive emotions for older people, whereas the effects of monetary support and non-material assistance are less pronounced. In addition, interpersonal interactions’ impacts are more significant for those who are female, older than 75 and with poorer health, as well as older people who exercise less frequently, have higher social status, and hold more traditional beliefs. In the current context of active promotion of healthy aging, findings of this paper have important implications for a deeper understanding and scientific management of depressive emotions among the elderly.

This paper uses a large scale and nationally representative dataset, Chinese General Social Survey, to empirically examine the role of physical activity in reducing the negative effects of depression among people with mental disorders. Empirical results demonstrate that physical exercise could help to alleviate depression's adverse consequences on work and life for depressed individuals. The impact mechanism is that physical activity may decrease the severity of depression, enhance life satisfaction, improve mood, and make people have a better sense of purpose and meaning in life. Therefore, from the perspective of multidimensional subjective wellbeing, evaluative wellbeing, experienced wellbeing and eudaimonic wellbeing all play mediating roles in the reduction of depression's adverse effects. Heterogeneity analysis shows that there are no significant gender differences in the health benefits of physical exercise, but its impact tends to be more prominent for depressed individuals who are younger and higher educated, with better health status, and live in urban areas. It is also found that socioeconomic status may play an important moderating role. The health benefits of physical activity seem to be greater for depressed people who have lower income, work in the secondary labor market, and have lower levels of social capital and assets. In addition, the instrumental variable approach is used to identify the causal impact of physical activity, which further proves a significant effect of it based on tackling the endogeneity problem. Meanwhile, this paper uses different explanatory and explained variables, different statistical models, as well as machine learning and placebo techniques to conduct robustness tests, all of which lend credence to above findings.