Beyond league tables: a multidimensional and rank-aggregation assessment of Saudi universities under vision 2030

Global university rankings increasingly influence higher education policy and institutional benchmarking, yet major ranking systems differ substantially in their indicators, weighting, normalization, and aggregation procedures. This raises questions about the robustness of conclusions drawn from a single league table. The issue is particularly relevant to Saudi Arabia, where the Human Capability Development Program under Saudi Vision 2030 establishes targets for increasing the number of Saudi universities among the global top 100 and top 200 and references QS, Times Higher Education (THE), and Shanghai Ranking. This study examines the consistency of QS, THE, and the Academic Ranking of World Universities (ARWU) in representing Saudi universities over the aligned 2022–2025 period. Cross-ranking coverage and positional agreement were assessed using Jaccard overlap, Spearman correlation, and Kendall correlation. Consensus rankings were constructed using Borda, median-rank, and Copeland aggregation, complemented by cross-ranking profile and Pareto assessment and sensitivity analyses under alternative weighting and rank-band specifications. QS and THE generally showed stronger positional agreement, reaching a Spearman correlation of 0.932 in 2025, whereas QS-ARWU agreement in the same year was weaker (ρ = 0.437). The three aggregation procedures produced broadly concordant consensus structures, although the leading position was method-sensitive in 2025: Borda ranked King Saud University first, whereas median-rank and Copeland ranked King Fahd University of Petroleum and Minerals first. Cross-ranking profile analysis further showed that King Abdulaziz University, King Saud University, and King Fahd University of Petroleum and Minerals were simultaneously non-dominated on the Pareto frontier in 2024 and 2025, indicating that a single consensus ordering can conceal differences across ranking-system outputs. Policy-threshold analysis likewise produced different Top-100 and Top-200 counts and institutional identities across the three systems. Overall, QS, THE, and ARWU provided related but non-equivalent representations of university standing. Combining cross-ranking comparison, rank aggregation, and cross-ranking profile assessment can therefore support more transparent monitoring and interpretation of ranking-based institutional and policy outcomes under Saudi Vision 2030.