Enterprise efficiency analysis based on explainable artificial intelligence: From predictive algorithms to mechanisms

Enhancing corporate efficiency is a critical challenge in management research due to the complexity inherent in multi-stage processes. This study analyzes data from 29,602 corporate samples spanning 2009-2023, using super-efficiency Data Envelopment Analysis to decompose corporate efficiency into innovation and commercialization stages. Our analysis identified four distinct efficiency clusters, with only 27.667% of firms achieving high performance in both stages. We developed predictive models using nine machine learning algorithms, XGBoost outperformed the others, predicting corporate efficiency clusters with an accuracy of 72.520%. SHAP analysis revealed that firm age, financing constraints, and largest shareholder equity are the most critical predictive factors. Bayesian network analysis uncovered two key pathways influencing corporate innovation efficiency: “largest shareholder ownership impacting employee size impacting enterprise efficiency´´ and “debt-to-asset ratio impacting employee size impacting enterprise efficiency´´. These findings demonstrate that changes in individual variables can trigger cascading effects through network transmission, ultimately affecting overall corporate efficiency. Consequently, governments and enterprises should adopt differentiated strategies based on actual efficiency performance to facilitate efficiency improvements.