Spatio-temporal differentiation and influencing factors of coupling coordination between digital economy and tourism economic resilience evidence from Guizhou China

Against the backdrop of profound restructuring in the global tourism industry and the rapid diffusion of digital technologies, an urgent question arises: how can underdeveloped regions achieve leapfrog improvements in tourism economic resilience (TER) through the digital economy (DE)? Existing research predominantly focuses on developed regions and rarely offers a systematic explanation of how the DE and tourism resilience interact and co-evolve in underdeveloped areas under resource constraints. Using Guizhou Province as a representative case, this study develops an analytical framework to examine the coupling and coordination between the DI and TER, based on panel data from 2014 to 2023. The main findings are as follows. (1) The degree of coupling coordination between the DE and TER in Guizhou exhibits a three-stage evolutionary pattern: “running-in and adaptation—rapid improvement—optimization and stabilization.” Overall, the system shifts from low-level dispersion to high-level agglomeration. Spatially, the pattern evolves from “high in the west, low in the east” to a multipolar structure characterized by “high in the west and relatively stable in the east,” with regional disparities gradually narrowing. (2) TER shows marked three-dimensional heterogeneity. Fundamental (supply-side) resilience is characterized by a dual-core driving structure; adaptive (operation-side) resilience improves relatively evenly across the province; and transformative (renewal-side) resilience displays periodic fluctuations. These differentiated resilience dimensions provide distinct forms of support to the coupling and coordination process. (3) Policy support, human capital endowment, and the degree of openness emerge as key influencing factors. The interaction between pairs of these factors exerts a non-linear, synergistic enhancement effect. Digital empowerment can realize its full effectiveness only when it is embedded within a favorable configuration of these external conditions. By focusing on an underdeveloped region, this study elucidates the internal mechanisms through which the DE enhances TER. Beyond empirical evidence, this paper further elaborates the differentiated co‑evolution logic between mountain lagging regions and coastal mature destinations: coastal areas rely on market‑driven spontaneous industry integration, while karst mountain provinces are constrained by fragmented tourism resources and digital divide; their coupling‑coordination improvement depends on policy‑led factor reallocation, and only multi‑factor nonlinear synergy can mitigate the path‑lock risk of single‑structure tourism industry.