Individualized second stage corrections in data envelopment analysis
提出一种个性化第二阶段修正方法，评估情境变量对每个决策单元绩效的影响，用于激励监管中的公平效率评估，鼓励运营商降低成本、提升服务质量和环保表现。
In the context of two-stage data envelopment analysis (DEA) for efficiency correction, we shift the focus from the common central tendency orientation in its second stage to an individually oriented procedure. We propose to evaluate the influence of contextual variables on each unit's performance relative to the other operating units. This results in an alternative approach in which the second stage inherits the principal property of DEA in the first stage of putting each individual unit in its best possible light. We demonstrate the applicability of our approach using data from the energy sector in the domain of incentive regulation, where operators are natural monopolies. In such systems of incentives, ensuring fair performance evaluations is crucial, given the influence of contextual variables beyond management control. Our approach contributes to efficiency correction procedures under these circumstances. The results not only encourage operators to economize costs and improve service quality but also motivate them to, for example, minimize environmental impact of operations, adopt eco-friendly technologies, and invest in renewable energy sources.