The central concept of strategic benchmarking is resource management efficiency, which ultimately results in profitability. However, little is known about performance measurement from resource-based perspectives. This study uses the data envelopment analysis (DEA) model with a dynamic network structure to measure the resource management and profitability efficiencies of 287 US commercial banks from 2010 to 2020. Furthermore, we provide frontier projections and incorporate five variables, namely capital adequacy, asset quality, management quality, earning ability, and liquidity (i.e., the CAMEL ratings). The results revealed that the room for improvement in bank performance is 55.4%. In addition, we found that the CAMEL ratings of efficient banks are generally higher than those of inefficient banks, and management quality, earnings quality, and liquidity ratios positively contribute to bank performance. Moreover, big banks are generally more efficient than small banks. Overall, this study continues the current heated debate on performance measurement in the banking industry, with a particular focus on the DEA application to answer the fundamental question of why resource management efficiency reflects benchmark firms and provides insights into how efficient management of CAMEL ratings would help in improving their performance.

中文翻译：

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从动态网络角度评估美国商业银行的资源管理和盈利效率

战略标杆管理的核心概念是资源管理效率，最终带来盈利能力。然而，人们对基于资源的绩效衡量知之甚少。本研究采用动态网络结构的数据包络分析（DEA）模型来衡量287家美国商业银行2010年至2020年的资源管理和盈利效率。此外，我们提供了前沿预测，并纳入了五个变量，即资本充足率、资产质量、管理质量、盈利能力和流动性（即 CAMEL 评级）。结果显示，银行绩效的改善空间为55.4%。此外，我们发现高效银行的CAMEL评级普遍高于低效银行，且管理质量、盈利质量和流动性比率对银行绩效有正向贡献。此外，大银行通常比小银行更有效率。总体而言，本研究延续了当前关于银行业绩效衡量的激烈争论，特别关注 DEA 应用程序，以回答为什么资源管理效率反映基准公司的基本问题，并深入了解 CAMEL 评级的有效管理将如何帮助提高他们的表现。