This study was conducted to systematically examine the causal impact and underlying mechanisms through which the new media usage behaviors of the population aged 60 and above affect their subjective well-being, within the context of China's accelerati...
This study was conducted to systematically examine the causal impact and underlying mechanisms through which the new media usage behaviors of the population aged 60 and above affect their subjective well-being, within the context of China's accelerating aging population and concurrent digital transformation. In an aging society, the digital adaptation of the elderly transcends mere technology adoption, holding multidimensional significance for maintaining social relationships, securing information access, and enhancing psychological welfare. Empirical analysis on this subject provides crucial implications for both policy design and theoretical development.
Methodologically, this research utilized balanced panel data constructed by merging three waves (2018, 2020, 2022) of the China Family Panel Studies (CFPS). The analysis comprised two main components: static and dynamic effect analyses. For the static analysis, panel fixed-effects models were employed to estimate the direct effects of new media usage status and the intensity of its use on subjective well-being. The mediating pathways were examined by disaggregating social support from adult children into economic, emotional, and instrumental support. For the dynamic analysis, a two-year lag balanced panel was constructed with 2018 as the baseline. Using the same fixed-effects models, the study analyzed the dynamic causal effects of changes in new media usage status (non-use→use, use→non-use, persistent use, persistent non-use) on subjective well-being. All analyses controlled for province-level regional effects and year effects. A comprehensive set of controls, including demographic, social, and economic variables, was applied to minimize endogeneity concerns.
The main empirical findings are summarized as follows. First, the static analysis revealed that a higher intensity of new media use (β=0.032, p<0.05) has a statistically significant positive effect on the subjective well-being of older adults. Older adults who use new media report higher subjective well-being than their non-using counterparts. Second, social support from children was found to have a partial mediating effect in the relationship between new media use and subjective well-being. Specifically, economic and emotional support functioned as positive mediating pathways, whereas instrumental support exhibited a negative mediating effect. This suggests that new media use may partially substitute for face-to-face interaction, implying a structural potential for reduced physical contact in caregiving contexts. Third, the dynamic analysis showed that, compared to the persistent non-use group, both the non-use→use (digital entry) group and the persistent use group exhibited higher subjective well-being. Notably, the well-being boost for the digital entry group (β=0.169) was approximately 1.8 times stronger than that for the persistent use group (β=0.092). The subjective well-being of the use→non-use (digital exit) group did not show a statistically significant difference from that of the persistent non-use group. Furthermore, heterogeneity analyses based on gender, region (urban/rural), and education level indicated that the positive static effects were more pronounced among female, urban-dwelling, and less-educated older adults.
The theoretical contribution of this study lies in proposing an integrated analytical framework that synthesizes Media Richness Theory, Interaction Ritual Chain Theory, and Social Support Theory to explain the multi-layered mechanisms through which new media usage influences well-being in later life. Methodologically, by combining static relationship analysis with dynamic change analysis, the study addresses limitations inherent in cross-sectional research and enhances the robustness of causal inference. In terms of policy implications, the findings provide empirical grounds for designing educational programs to promote digital inclusion among the elderly, supporting family policies that strengthen intergenerational communication, and building infrastructure to bridge the urban-rural digital divide.