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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in August 2026 (Volume 31, Issue 2) Submit manuscript

Artificial intelligence as a strategic dynamic capability for enhancing triple bottom line performance in MSMES

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  • Artificial intelligence as a strategic dynamic capability for enhancing triple bottom line performance in MSMES

Komal. S * and Deeksha S

Faculty, Department of Studies and Research in Commerce, Dr Manmohan Singh Bengaluru City University, Commerce Block, Bengaluru – 560001, Karnataka, India.
ORCID Details
Komal. S ORCID NO: 0009-0004-9066-187X
Deeksha S ORCID NO: 0009-0003-9099-3228

Research Article

 

World Journal of Advanced Research and Reviews, 2026, 31(02), 769–780

Article DOI: 10.30574/wjarr.2026.31.2.2031

DOI url: https://doi.org/10.30574/wjarr.2026.31.2.2031

Received on 22 June 2026; revised on 01 August 2026; accepted on 03 August 2026

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.

Artificial Intelligence; Dynamic Capabilities; Resource-Based View; Sustainable Business Model Innovation; Triple Bottom Line; MSMES; Digital Transformation; Sustainability; Strategic Capabilities; Structural Equation Modelling (SEM)

https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2026-2031.pdf

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Komal. S and Deeksha S. Artificial intelligence as a strategic dynamic capability for enhancing triple bottom line performance in MSMES. World Journal of Advanced Research and Reviews, 2026, 31(02), 769–780. Article DOI: https://doi.org/10.30574/wjarr.2026.31.2.2031

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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