A Multi-Platform Online Data-Driven Diagnostic Approach for Macro-Level Sustainability of Homestays

The increasing impact of online popularity on consumption calls for integrated sustainability diagnostic frameworks that combine both consumption and production data. This study aims to develop a macro sustainability diagnostic model integrating multi-platform online data and to tackle the challenges of scale, accuracy, and cost in evaluating tourism industries. The model comprises three primary indices: the industry scale index, the social attention index, and the type reference index. It proposes an interpretative and concise development typology including externally dependent, outward spillover, and coordinated types. Using homestay data from three online platforms and the spatial analysis methods of ArcGIS, this study validates the model’ effectiveness in China. It also reveals significant spatiotemporal heterogeneity and four macro influencing factors of the Chinese homestay industry’s sustainability. This study contributes to the methodological and typological frameworks for the sustainability diagnosis, as well as the theoretical understanding of Chinese homestays’ spatiotemporal evolution. It also provides a scientific basis for the rational planning and transformation of China’s homestay industry in the digital tourism economy. The discussion on the model’s limitations regarding data resources and micro-validity provides new insights for future sustainability assessments of other related industries in more regions in the digital era.