Exploring knowledge benchmarking using time-series directional distance functions and bibliometrics

For strategic reasons, benchmarking best practices from efficient competitors is not usual in many data envelopment analysis (DEA) applications. Even for industries composed of multiple branches, providing information about efficient practices for their peers can jeopardize results for those branches if they compete for market, resources or recognition by the central administration. In this work, a time-series adaptation for the DEA directional model is proposed as an alternative for coping with this problem. The methodological approach has three stages for this benchmarking to occur: Data, Information and Knowledge Extraction. In the first stage, we compare the same unit in different moments to identify efficient periods instead of efficient competitors. As a result, successful performance strategies are investigated using the bibliometric coupling of employees' relevant statements in the second and third stages. The application in a branch of the Brazilian Federal Savings Bank allowed an internal benchmarking of efficient periods when specific performance incentives, innovative processes, competitive strategies, and human resource changes were adopted for improving the unit's performance.

Automated summary

For strategic reasons, benchmarking best practices from efficient competitors is not common in DEA applications. This study proposes a time-series adaptation of the DEA directional model as an alternative to address this problem and suggests a three-stage approach for benchmarking.