Efficiency Analysis of Intellectual Capital Under Deep Uncertainty: A Robust DEA Approach

Pejman Peykani (), Mojtaba Nouri (), Seyed Ehsan Shojaie (), Fatemeh Ghiyaasi and Amir Esmaeli
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Pejman Peykani: Khatam University
Mojtaba Nouri: Iran University of Science and Technology
Seyed Ehsan Shojaie: Iran University of Science and Technology
Fatemeh Ghiyaasi: Islamic Azad University
Amir Esmaeli: Khatam University

A chapter in Advances in the Theory and Practice of Data Envelopment Analysis, 2025, pp 27-41 from Springer

Abstract: Abstract This research introduces a robust data envelopment analysis (RDEA) methodology to evaluate intellectual capital efficiency in environments of deep uncertainty. Intellectual capital, a vital intangible asset for organizational success, is challenging to measure due to its unpredictable and variable nature. The proposed approach utilizes conservative robust optimization with a box uncertainty set to address these complexities effectively, ensuring more reliable and stable efficiency scores across diverse scenarios. The methodology accommodates variations in input and output data while exploring the interrelationships among key dimensions of intellectual capital holistically and with nuanced insights. Empirical validation using a sample of organizations demonstrates the practical applicability of the RDEA framework, highlighting its ability to handle uncertainties and account for varying returns to scale. This study contributes to the field by offering a versatile and robust tool for assessing intellectual capital efficiency, supporting more informed and resilient decision-making processes in uncertain contexts. The findings provide actionable insights and a solid foundation for advancing intellectual capital management practices.

Keywords: Intellectual Capital; Data Envelopment Analysis; Deep Uncertainty; Efficiency Assessment; Conservative Robust Optimization; Box Uncertainty Set (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-98177-7_3

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DOI: 10.1007/978-3-031-98177-7_3

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