This paper provides rigorous analytical discussions of the dual-role factor classification problem in Data Envelopment Analysis (DEA). We study two approaches taking opposite directions: one (the conventional and popular approach) clarifies the input/output roles prior to analysis, while the other incorporates the dual roles into the analysis. We show that both approaches are necessary and related. The former requires modification and the latter is a special empirical implication of the former after modifying. Two pitfalls are discussed, i.e., providing underestimated efficiency and a benchmark contradicting the role classification. Finding pitfalls in the form of observing slacks in the empirical implementation applying the standard DEA models with pre-classified roles, we suggest that a Pareto–Koopmans efficiency measure can help avoid unmeaningful outcomes even without considering the interaction among the dual-role factor and other inputs and outputs.

本文对数据包络分析 (DEA) 中的双重角色因素分类问题进行了严格的分析讨论。我们研究了两种相反方向的方法：一种（传统和流行的方法）在分析之前阐明输入/输出角色，而另一种将双重角色纳入分析。我们表明这两种方法都是必要的和相关的。前者需要修正，后者是前者修正后的特殊经验蕴涵。讨论了两个陷阱，即提供低估的效率和与角色分类相矛盾的基准。在应用具有预分类角色的标准 DEA 模型的经验实施中以观察松弛的形式发现陷阱，