This research emphasizes the potential influences of social community environments on low-income employees’ mental health. Using a two-wave panel design, we collect 218 matched data from low-income employees in Harbin City, China. We developed a moderated mediation model to test our hypotheses with the following significant results: (1) neighboring behavior, defined as both giving and receiving various kinds of assistance to and from one’s neighbors, positively influenced mental health; (2) work-family conflict mediated the relationship between neighboring behavior and mental health; (3) gender moderated the influences of neighboring behavior on mental health, such that neighboring behavior had a stronger positive influence on mental health for females than for males; (4) gender moderated the mediating effect of work-family conflict; that is, the positive influences of neighboring behavior were stronger for female employees than for male employees. This research explores the mechanism and boundary conditions of the relationship between neighboring behavior and mental health. In practice, community managers support community social workers by organizing community-building social activities and supportive programs to enhance residents’ neighboring behavior.

This study investigated the relationships between received neighboring behavior (i.e., informal mutual assistance and information sharing among neighbors) and the general health of low-income residents in a mixed-income community. A conservation resource theory perspective was applied. Survey data were selected from a self-reported questionnaire of 247 low-income residents in a mixed-income community of China. A 2-wave panel design was used to test the hypothesized relationships among the studied variables, and the interval between both measurements was 5 weeks. Structural equation modeling was adopted to examine the proposed hypotheses. The findings showed that received neighboring behavior decreased individual work-family conflict, while showing no significant differences between the mediating effects of work-family conflict. This study has important implications for neighbor behavior and work-family conflict.

Background: We examined the effects of supportive neighboring behavior on mental health and career satisfaction among Chinese low-income employees. We further examined the mediating roles of work interference with family (WIF) and of family interference with work (FIW) in this relationship. Methods: A total of 220 Chinese low-income employees were selected via two-wave longitudinal survey in China; the time distance was five weeks. They completed questionnaires on their self-reported supportive neighboring behavior, work-family conflict, general mental health and career satisfaction. Afterwards, we adopted a structural equation modeling (SEM) to examine our hypotheses by R (Version 3.5.3) and Lavaan Package (Version 0.6-3). Results: Supportive neighboring behavior (Time 1) improved good mental health (Time 2) and career satisfaction (Time 2). Work interference with family (Time 1) mediated the effect of neighboring behavior on mental health while family interference with work (Time 1) mediated the effect of neighboring behavior on mental health and career satisfaction. Conclusion: Our findings suggest that supportive neighboring behavior is vital in mitigating mental health problems and enhancing career satisfaction by decreasing work interference with family and family interference with work. Our research expands the scope of current literature on community support by incorporating bi-directional supportive neighboring behavior. By adopting family interference with work and work interference with family as mediators, our research examines the spillover mechanisms through which career satisfaction and mental health are influenced by supportive neighboring behavior.

To examine how cross-strata neighboring behavior in a mixed-income community can influence the consumption choices of individuals in low-income groups, and to improve the prediction accuracy of the consumption choice model of low-income groups for small sample sizes, we developed a support vector machine (SVM) algorithm based on the influence of neighboring behavior. We substituted the predicted latent variables into the SVM classifier and constructed an SVM prediction model with latent variables based on reference group theory. We established the model parameters using cross-validation and used low-income residents from a mixed-income community in Shanghai as study objects to empirically test the model’s performance. The results show that the SVM selection model with latent variables has good prediction accuracy. The proposed model’s accuracy was improved by 1.29% on the basis of the particle swarm optimization (PSO)-SVM model without latent variables, and by 19.35% on the basis of the SVM model with latent variables. The proposed model can be employed to predict the consumption choices of individuals in low-income groups. This paper offers a theoretical reference for investigating neighboring behavior in a mixed-income community and the consumption choices of individuals in low-income groups and is practically important for urban community planning systems.