A novel centralized cross-efficiency evaluation via explainable artificial intelligence in the context of big data

Cross-efficiency evaluation in data envelopment analysis (DEA) assumes that decision making units (DMUs) have full flexibility in choosing weights according to their individual preferences. However, this total autonomy may be inapplicable in some centralized organizational scenarios. To address this problem, this paper introduces a novel centralized cross-efficiency evaluation which considers both individual and organizational preferences with assistance of explainable artificial intelligence (XAI) in the context of big data. Specifically, XAI is first applied to approach the organizational efficiency function and then calculate the marginal contribution of each variable as the variable importance, which represents the organizational preference. Furthermore, we propose a centralized secondary goal model to select the unique optimal weight profile from the candidate weights that remain self-efficiency as Pareto-optimal, such that the deviation between individual and organizational preferences is minimized. In addition, a centralization factor is introduced to ensure that the model's centralization degree corresponds to the actual centralization level in organizational management. Finally, the proposed method is applied to evaluate the efficiency of DMUs within three different centralized organizations sequentially. The results verify that the proposed method yields more discriminative and robust efficiency scores within organizations compared to several previous cross-efficiency evaluation methods.