Dynamic predictive modeling of basic psychological need satisfaction on subjective well-being among university students: examining the boundary conditions of self-determined motivation.

Drawing on self-determination theory, this study develops a dynamic predictive framework to examine how basic psychological need satisfaction prospectively predicts subjective well-being among Chinese undergraduates and how self-determined motivation operates as a boundary condition. Five waves of panel data were collected from 547 students at four eastern Chinese universities across one academic year, with measurements spaced approximately ten weeks apart. Latent growth curve modeling combined with cross-lagged panel analysis revealed that need satisfaction at one wave reliably forecast subsequent well-being beyond autoregressive carryover, with autonomy exerting the strongest contemporaneous influence and relatedness contributing more enduring lagged effects. The interaction between lagged need satisfaction and the relative autonomy index was statistically reliable, and Johnson-Neyman analysis localized a non-significance interval of [-2.81, 1.93], outside which the predictive association held for roughly 71% of participants. Conditional slopes rose from 0.092 at low motivation to 0.418 at high motivation, suggesting that self-determined regulation statistically conditions, rather than uniformly amplifies, the need-well-being association. Robustness checks across alternative measures, estimators, and demographic strata supported the stability of these patterns. Because the design is observational, we read these patterns as temporally ordered associations rather than causal effects; traditional and random-intercept cross-lagged specifications, together with longitudinal measurement invariance tests, converged on the same qualitative picture. The findings add a dynamic, within-academic-year perspective to self-determination theory and inform mental health practice in Chinese higher education by suggesting that need-supportive interventions are most effective when paired with attention to motivational quality.