2011•Benchmarking An International JournalRequires access

Benchmarking with data envelopment analysis: a return on asset perspective

Seong Jong Joo, Don Nixon, Philipp A. Stoeberl

Open publisher page 58 citations

Abstract

Purpose Selecting appropriate variables for analytical studies is critical for the validity of analysis. It is the same with data envelopment analysis (DEA) studies. In this study, for benchmarking using DEA, the paper seeks to suggest a novel framework based on return on assets (ROA), which is popular and user‐friendly to managers, and demonstrate it by use of an example. Design/methodology/approach The paper demonstrates the selection of variables using the elements of ROA and applies DEA for measuring and benchmarking the comparative efficiency of companies in the same industry. Findings It is frequently impossible to obtain internal data for benchmarking from competitors in the same industry. In this case, annual reports may be the only source of data for publicly traded companies. The framework demonstrated with an example is a practical approach for benchmarking with limited data. Research limitations/implications This study employs financial data and is subject to the limitations of accounting practices. Originality/value The approach is applicable to various studies for performance measurement and benchmarking with minor modifications. Contributions of the study are twofold: first, a framework for selecting variables for DEA studies is suggested; second, the applicability of the framework with a real‐world example is demonstrated.

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What this paper is about

Purpose Selecting appropriate variables for analytical studies is critical for the validity of analysis. It is the same with data envelopment analysis (DEA) studies. In this study, for benchmarking using DEA, the paper seeks to suggest a novel framework based on return on assets (ROA), which is popular and user‐friendly to managers, and demonstrate it by use of an example. Design/methodology/approach The paper demonstrates the selection of variables using the elements of ROA and applies DEA for measuring and benchmarking the comparative efficiency of companies in the same industry. Findings It is frequently impossible to obtain internal data for benchmarking from competitors in the same industry. In this case, annual reports may be the only source of data for publicly traded companies. The framework demonstrated with an example is a practical approach for benchmarking with limited data. Research limitations/implications This study employs financial data and is subject to the limitations of accounting practices. Originality/value The approach is applicable to various studies for performance measurement and benchmarking with minor modifications. Contributions of the study are twofold: first, a framework for selecting variables for DEA studies is suggested; second, the applicability of the framework with a real‐world example is demonstrated.

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Available abstract

Purpose Selecting appropriate variables for analytical studies is critical for the validity of analysis. It is the same with data envelopment analysis (DEA) studies. In this study, for benchmarking using DEA, the paper seeks to suggest a novel framework based on return on assets (ROA), which is popular and user‐friendly to managers, and demonstrate it by use of an example. Design/methodology/approach The paper demonstrates the selection of variables using the elements of ROA and applies DEA for measuring and benchmarking the comparative efficiency of companies in the same industry. Findings It is frequently impossible to obtain internal data for benchmarking from competitors in the same industry. In this case, annual reports may be the only source of data for publicly traded companies. The framework demonstrated with an example is a practical approach for benchmarking with limited data. Research limitations/implications This study employs financial data and is subject to the limitations of accounting practices. Originality/value The approach is applicable to various studies for performance measurement and benchmarking with minor modifications. Contributions of the study are twofold: first, a framework for selecting variables for DEA studies is suggested; second, the applicability of the framework with a real‐world example is demonstrated.

Key concepts: Benchmarking, Data envelopment analysis, Competitor analysis, Computer science, Asset (computer security), Performance measurement, Return on assets, Econometrics

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