Big data and predictive analytics capability and triple bottom line sustainability performance: a resource‐based perspective from BRICS nations

Kugara, G, Khan, S.A, Subhani, F, Papadopoulos, T and Acquaye, A (2026) 'Big data and predictive analytics capability and triple bottom line sustainability performance: a resource‐based perspective from BRICS nations.' Business Strategy and the Environment. e71763.

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Abstract

This study aims to investigate the relationship between big data and predictive analytics (BDPA) capability and triple bottom line (TBL) sustainability performance in BRICS manufacturing firms. Grounded in the resource‐based view (RBV), we conceptualise BDPA capability as a third‐order hierarchical construct comprising tangible resources (data infrastructure and technology), intangible resources (organisational culture, organisational learning and data‐driven culture) and human‐related skills (technical skills and management skills). Survey data have been used from 304 IT and analytics professionals representing different organisations across BRICS economies and partial least squares structural equation modelling (PLS‐SEM) with 5000 bootstrap samples; we test six hypotheses examining both the resource‐capability link and the capability‐performance link. Results reveal that all three resource categories significantly contribute to BDPA capability, with human‐related skills exerting the strongest influence. BDPA capability, in turn, significantly predicts environmental performance, social performance and economic performance, explaining 40%, 50% and 55% of variance, respectively. The study contributes to the literature by providing an empirically validated, multi‐level resource‐capability‐performance framework for analytics‐driven sustainability in emerging economies.

Item Type:	Article
Keywords:	big data and predictive analytics, BRICS, higher-order construct, PLS-SEM, resource-based view, sustainability performance, triple bottom line
Divisions:	Bath Business School
Date Deposited:	10 Nov 2026 10:20
Last Modified:	10 Nov 2026 10:27
ISSN:	0964-4733
URN:	https://researchspace.bathspa.ac.uk/id/eprint/18069

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