Publication: Sustainability Reporting: A Machine Learning Meta-Regression Analysis
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Issued Date
2026-01-01
Resource Type
eISSN
22277080
Scopus ID
2-s2.0-105029085444
Journal Title
Technologies
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Technologies Vol.14 No.1 (2026)
Suggested Citation
Daovisan H. Sustainability Reporting: A Machine Learning Meta-Regression Analysis. Technologies Vol.14 No.1 (2026). doi:10.3390/technologies14010021 Retrieved from: https://hdl.handle.net/20.500.14740/55300
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Abstract
The quality of sustainability reporting (SR) has come to be widely regarded as a factor of considerable importance in influencing organisational performance. However, empirical evidence in relation to SR has been characterised by fragmentation across industrial sectors. The purpose of this study was to synthesise the relationship between SR and organisational performance across the manufacturing, finance, energy and utilities, services, and ICT sectors. Our systematic review, performed using the PRISMA 2020 framework and machine learning meta-regression, was conducted on 372 studies retrieved from the Scopus database between 1 January 2020 and 1 November 2025. Our pooled correlation showed that the SR effect was positively associated with outcome performance (r = 0.231, 95% CI [0.184, 0.279]) and yielded a standardised mean difference (g = 0.426, 95% CI [0.341, 0.512]). The meta-regression showed that assurance quality (β = 0.156, p < 0.001), the regulatory regime (β = 0.142, p < 0.001), and reporting standard alignment (β = 0.118, p = 0.003) are significant moderating factors. The predictive robustness was confirmed through cross-validation (R<sup>2</sup> = 0.55; RMSE = 0.056), while feature stability was substantiated by a mean SHAP variance of less than 0.012. Transparency, comparability, and decision usefulness in SR were found to be enhanced by institutional mechanisms—particularly those providing credible assurance within mandatory regulatory frameworks.