Revisiting Dual-Role Factors in DEA: Derivation and Implications

Abstract

Data Envelopment Analysis (DEA) is a mathematical programming method to evaluate relative performance. Typical DEA studies consider a production process transforming inputs to outputs. In some cases, however, some factors can be both inputs and outputs simultaneously, and are termed dual-role factors. For example, research funding can be an input that strengthens a university’s academic performance and the actual funds can be an output. This paper investigates the problem of how to incorporate dual-role factors in DEA. Rather than proposing an ad hoc evaluation model directly, we propose a joint technology by summarizing the intuitive thinking. The efficiency evaluation models, based on variant assumptions, thus can be axiomatically derived, validated and extended. We show how to determine the input/output tendency of a dual-role factor based on the evaluating results and explained from different aspects. We conclude that the tendency is a property on the projected boundary, not the data point itself.