Abstract
OBJECTIVE: This cross-sectional empirical study evaluates the Chinese Hot Spring Medical and Long-term Care Integration with Rehabilitation Services Model (Hot Spring MLR Model). By quantifying the demographic characteristics, behavioral determinants, and multidimensional value perceptions of the target population ( N = 218 ), this research elucidates developmental bottlenecks and proposes evidence-based optimization strategies. METHODS: Primary empirical data were acquired from target users of the Hot Spring MLR Model ( N = 218 ) utilizing a stratified random sampling strategy. The structured instrument evaluated demographic profiles, behavioral determinants, and dual-dimensional Importance-Performance Analysis (IPA) metrics. Predictors of consumption tiers were isolated via univariate screening followed by generalized ordered logistic regression modeling. Furthermore, paired t -tests and IPA matrices were implemented to systematically quantify the divergence between perceived importance and actual service performance. RESULTS: In the target population survey, the sub-health population constitutes the primary target group for the Hot Spring Medical and Long-term Care Integration with Rehabilitation Services Model (44.5%), with the highest participation rate observed in the traditional Chinese medicine (TCM) of Hot Spring Medical and Long-term Care Integration with Rehabilitation Services Model (82.1%). The consumption behavior within the Hot Spring Medical and Long-term Care Integration with Rehabilitation Services Model is significantly correlated with age, education, consumption motivation, and social media marketing channels (p < 0.05). The Importance-Performance Analysis (IPA) of the Hot Spring Medical and Long-term Care Integration with Rehabilitation Services Model reveals a positive correlation between the importance and performance of each sub-dimension. Service attitude demonstrates the highest importance, while professional-quality exhibits the highest performance. Significant discrepancies exist between the importance and performance of the dimensions of affordable pricing and convenient transportation. CONCLUSION: Empirical evidence indicates that the optimal service configuration for the Hot Spring MLR Model lies in preventive interventions and holistic health management for sub-health populations. Furthermore, mitigating the quantified behavioral determinants and resolving the supply-demand discrepancies identified via the IPA matrix provide a strictly data-driven framework to optimize resource allocation and enhance overall service efficacy.