Measuring Corporate Global Performance: Can AI Overcome Methodological Biases?

Mesure de la performance globale de l'entreprise: L'IA peut-elle lever les biais méthodologiques

Othmane Khanoufi and Asmae Fellaji
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Othmane Khanoufi: ENCG - Ecole Nationale de Commerce et de Gestion - UH2C - Université Hassan II de Casablanca = University of Hassan II Casablanca = جامعة الحسن الثاني (ar)
Asmae Fellaji: ENCG - Ecole Nationale de Commerce et de Gestion - UH2C - Université Hassan II de Casablanca = University of Hassan II Casablanca = جامعة الحسن الثاني (ar)

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Abstract: This article aims to examine the extent to which artificial intelligence (AI) is mobilized by researchers to overcome the limitations associated with analyzing the relationship between corporate social performance (CSP) and financial performance (FP), particularly those related to limited human capabilities and methodological biases that may be introduced by analysts, such as non-representative samples or the neglect of the temporal dimension. A systematic literature review was conducted following the PRISMA protocol, based on the Scopus database, covering the period 2012–2025 and limited to articles published in English or French. The analysis reveals the absence of studies simultaneously integrating AI, CSP, and FP, or addressing this relationship from a corporate sustainability performance (CSP/overall performance) perspective. However, four articles using AI to measure sustainable performance were identified, although they do not explicitly examine the CSP–FP relationship. These studies primarily rely on artificial neural networks to extract information from unstructured textual documents, account for a multidimensional set of variables, and improve or replace conventional statistical models. The findings highlight the limitations of current AI applications in this field while emphasizing its potential to reduce certain methodological biases. In particular, they suggest the relevance of hybrid approaches combining traditional econometric models in order to move beyond the linearity assumptions commonly adopted in empirical analyses and to identify more complex relationships.

Keywords: Global Performance.; Social Performance; Financial Performance; Artificial Intelligence; Performance globale.; Performance sociale; Performance financière; Intelligence Artificielle (search for similar items in EconPapers)
Date: 2026-04-15
Note: View the original document on HAL open archive server: https://hal.science/hal-05592435v1
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Published in International Journal of Accounting, Finance, Auditing, Management and Economics, 2026, 07 (04 (2026)), pp.539-558. ⟨10.5281/zenodo.19462669⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05592435

DOI: 10.5281/zenodo.19462669

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