What question did this study set out to answer?

To identify the factors contributing to tourism satisfaction gaps between urban and rural destinations.

March 21, 2026Open Access

Bridging the Urban–Rural Tourism Satisfaction Gap: A Service Capacity Perspective on Territorial Development Challenges

Puntos clave

To identify the factors contributing to tourism satisfaction gaps between urban and rural destinations.
Text-mining analysis of 33,174 attractions across 349 Chinese cities
Sentiment measurement using independent dictionary-based methods and BERT transformer
SHAP decomposition for policy implications
Propensity score matching to confirm rural tourism penalties
Rural and urban attractions under-promise, with urban attractions generating more positive surprises
Controllable service indicators explain over 70% of the variance in satisfaction
A 0.074–0.100-point rural penalty remains after controlling for confounders
Baseline capacity issues, not investment efficiency, represent the real challenge for rural tourism

Resumen

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods (r=0.58, p<0.001) and cross-paradigm verification using a pre-trained BERT transformer (τ=1.000 ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness.

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