Artificial intelligence as a strategic dynamic capability for enhancing triple bottom line performance in MSMES
TL;DR
The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources, thereby extending RBV and DCT within the sustainability and digital transformation domains.
Abstract
Purpose: This study examines how Artificial Intelligence (AI) functions as a strategic dynamic capability that enhances Triple Bottom Line (TBL) performance in Micro, Small and Medium Enterprises (MSMEs). By integrating the Resource-Based View (RBV), Dynamic Capability Theory (DCT) and the Triple Bottom Line framework, the study develops and validates a capability-driven model explaining how AI-enabled organizational transformation fosters sustainable business model innovation and sustainable performance. Design/methodology/approach: A quantitative, cross-sectional research design was adopted using survey data collected from 348 Indian MSMEs across manufacturing and service sectors. Structural Equation Modelling (SEM) using SmartPLS 3.0 was employed to examine the direct, indirect and sequential relationships among AI capability, dynamic capabilities, sustainable business model innovation (SBMI) and the three dimensions of TBL performance. Findings: The findings demonstrate that AI capability significantly strengthens organizational dynamic capabilities, which subsequently promote sustainable business model innovation and improve economic, environmental and social performance. AI also exhibits significant direct effects on TBL dimensions; however, the strongest influence occurs through the sequential mediation of dynamic capabilities and SBMI. The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources. Research limitations/implications: The cross-sectional design limits causal inference, and the findings are specific to Indian MSMEs. Future research may employ longitudinal designs, comparative international studies and sector-specific analyses to examine the evolution of AI-enabled sustainability capabilities. Practical implications: The study provides strategic guidance for MSME leaders and policymakers by demonstrating that investments in AI should be complemented by capability development and sustainable business model transformation to maximize long-term value creation and resilience. Originality/value: This research advances strategic management literature by conceptualizing AI as a higher-order dynamic capability rather than a standalone technology. It offers a novel mechanism-based explanation linking AI capability, dynamic capabilities and sustainable business model innovation to Triple Bottom Line performance, thereby extending RBV and DCT within the sustainability and digital transformation domains.