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<p>Research and Development (R&amp;D) efficiency plays a critical role in fostering industrial competitiveness and driving sustainable economic growth. Traditional Data Envelopment Analysis (DEA) models, while widely used for evaluating R&amp;D productivity, often suffer from limited discrimination power and an inability to capture the complex interdependencies among inputs and outputs. This study introduces a Multi-Stage DEA–ARIII (Assurance Region Type III) model, which enhances traditional DEA by incorporating correlation-based constraints to improve the differentiation of decision-making units (DMUs). By structuring R&amp;D performance into a two-stage ARIII model, we evaluate efficiency in budget allocation (Stage&#xa0;1) and scientific and manufacturing output generation (Stage&#xa0;2). The proposed ARIII Multi-Stage DEA model accounts for interdependencies between inputs and outputs, thereby ensuring a more granular and realistic efficiency assessment. Our empirical analysis, conducted across multiple European and emerging economies, reveals substantial disparities in R&amp;D efficiency, identifying high-performing and underperforming countries. The findings provide actionable insights for operations managers, policymakers, and industry leaders, enabling strategic resource allocation and targeted improvements in R&amp;D-driven industrial and educational operations. This study advances the application of DEA in operations management, offering a robust framework for benchmarking R&amp;D performance while ensuring a balance between financial investment and industrial output.</p>

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Assessing R&D efficiency in industry: a multi-stage-ARIII approach for enhanced performance evaluation

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Konstantinos Petridis,
  • Mehmet Güray Ünsal,
  • Altay Oğulcan Dalkılıç

摘要

Research and Development (R&D) efficiency plays a critical role in fostering industrial competitiveness and driving sustainable economic growth. Traditional Data Envelopment Analysis (DEA) models, while widely used for evaluating R&D productivity, often suffer from limited discrimination power and an inability to capture the complex interdependencies among inputs and outputs. This study introduces a Multi-Stage DEA–ARIII (Assurance Region Type III) model, which enhances traditional DEA by incorporating correlation-based constraints to improve the differentiation of decision-making units (DMUs). By structuring R&D performance into a two-stage ARIII model, we evaluate efficiency in budget allocation (Stage 1) and scientific and manufacturing output generation (Stage 2). The proposed ARIII Multi-Stage DEA model accounts for interdependencies between inputs and outputs, thereby ensuring a more granular and realistic efficiency assessment. Our empirical analysis, conducted across multiple European and emerging economies, reveals substantial disparities in R&D efficiency, identifying high-performing and underperforming countries. The findings provide actionable insights for operations managers, policymakers, and industry leaders, enabling strategic resource allocation and targeted improvements in R&D-driven industrial and educational operations. This study advances the application of DEA in operations management, offering a robust framework for benchmarking R&D performance while ensuring a balance between financial investment and industrial output.