Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach

Abstract

The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framework, the group metatechnology ratio (MTR) is an important indicator to help measure the technology gap at the group level. The common approach to the group MTR aggregation is the arithmetic average approach, which requires the MTR of the DMU to satisfy the ‘additive independence’ condition. Because the MTRs are generated from the same data set and linked to each other, the MTRs do not meet the ‘additive independence’ condition. Therefore, a new aggregation approach is needed. This study applies the evidential reasoning (ER) approach to aggregate the group MTR by the transformation of the MTR of DMU to pieces of evidence. Moreover, this study proposes examples that verify the applicability and practicality of the MTR aggregation using the ER approach, including an empirical example of the evaluation practice of the transportation system of 30 provincial regions in mainland China for 2013–2019.

Keywords:

• Data envelopment analysis
• metafrontier analysis
• metatechnology ratio
• aggregation
• evidential reasoning approach

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

1 In the context of multiple outputs, the calculation method 1 necessitates price information, which is unfortunately unavailable for this empirical application. However, the example has demonstrated that the selection of weight does not impact the utilization of the ER method. Therefore, we adopt the equal weight approach, consistent with previous literature (e.g., Liu and Lin 2018; Feng and Wang 2018).

Additional information

Funding

This research was supported by the National Natural Science Foundation of China (Nos. 71991464 and 71921001), Major Project of the National Social Science Fund of China (18ZDA064), Anhui Provincial Natural Science Foundation (2108085MG238) and the Fundamental Research Funds for the Central Universities (WK2040000027).