Explainable DEA–ensemble approach with golden jackal optimization: efficiency evaluation and prediction for United States information technology firms

Abstract

This study presents an integrated Data Envelopment Analysis (DEA) and ensemble learning framework optimized with the Golden Jackal Optimization (GJO) algorithm to evaluate and predict the efficiency of United States information technology firms. Both Constant Returns to Scale and Variable Returns to Scale models were applied to measure firm efficiency and compute scale efficiency, providing a clearer distinction between managerial and scale-related effects. Using data from 3,940 firms over the period 2013 to 2023, a robustness test introducing ±20% random noise to a 10% random sample confirmed that the CCR model achieved stronger stability, with a correlation coefficient of 0.795 compared to 0.773 for the BCC model. Consequently, the CCR results were adopted as the basis for predictive modeling. DEA efficiency scores were predicted using six ensemble learners, including XGBoost, Gradient Boosting Regressor, AdaBoost, Extra Trees Regressor, Random Forest, and LightGBM, with GJO employed for hyperparameter tuning. The Gradient Boosting Regressor optimized with GJO achieved the best predictive performance, accurately reproducing the observed efficiency scores. SHAP and feature importance analyses revealed that Total Equity, Operating Income, and Total Assets were the most influential determinants of efficiency. This research contributes a scalable and interpretable approach to efficiency prediction, offering actionable insights for managers, investors, and policymakers in volatile financial markets.