A Two-Stage Bayesian Ordinal Model with Rank-Based Fuzzy Evidence for Cross-Country Ride-Hailing Service Improvement

Multilingual online reviews combine ordinal ratings, asymmetric positive and negative evidence, sparse attribute occurrence, and substantial cross-country imbalance. This study presents an integrated inferential framework. Signed topic scores are converted to within-country rank intensities, and country-specific cumulative-logit models distinguish positive and negative occurrence baselines from their corresponding intensity contrasts. The two intensity contrasts are then synthesized jointly through a bivariate Bayesian normal-normal random-effects model that retains their within-country covariance. The primary analysis uses all 30,042 reviews observed in the common 2019–2024 window; the complete 85,373-review corpus and repeated country-capped samples are sensitivity analyses. Separating the two occurrence baselines improves summed AIC from 32,347.7 to 32,167.8, while a transformation-by-function comparison shows that natural splines improve AIC and quadratic-weighted agreement. The linear-rank model is retained to provide comparable scalar intensity contrasts. Targeted partial proportional-odds fits substantially improve in-sample AIC in China and Japan but leave repeated-validation performance and all nine average effect directions essentially unchanged. A secondary semantic mapping audit agrees with 30 of 31 topic assignments. Simulation results show generally adequate interval coverage but reduced Kano-state accuracy under small K, sparse occurrence, and proportional-odds violations. The findings therefore support tiered, uncertainty-aware prioritization rather than a deterministic global ranking.