Big Data-Driven Evaluation of University Research Performance: A DEA-BCC-Malmquist Analysis for Regional Innovation in Higher Education

Universities shoulder the dual mission of advancing scientific and technological prowess while cultivating talent. With increased research funding under China’s 13th and 14th Five-Year Plans, a critical question emerges: does sustained investment guarantee uniformly improved outcomes? This study adopts a big data-driven DEA-BCC-Malmquist approach to evaluate university research performance through a regional innovation lens, developing an evaluation system that incorporates science and technology commercialization metrics alongside traditional input-output indicators. Key findings reveal suboptimal research performance nationwide, marked by volatility and pronounced regional disparities, with technological progress change efficiency identified as the primary constraint on total factor productivity. To address these challenges, the study proposes targeted strategies, including establishing benchmark university science and technology innovation hubs to enhance regional collaboration, implementing flexible research funding allocation mechanisms, and deepening performance-oriented reforms in research funding management systems. These recommendations provide actionable pathways for elevating university research capabilities through data-centric governance.