Aug 2026· World Journal of Advanced Research and Reviews· 0 citations
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.
As a strategic general-purpose technology reshaping the manufacturing landscape, artificial intelligence (AI) holds significant potential to drive green transformation. However, how AI translates into green transformation through organizational capabilities has received limited attention. This study examines how AI drives green transformation in manufacturing enterprises through dynamic capabilities—specifically, absorptive, innovative, and adaptive capabilities. Drawing on the resource-based view and dynamic capabilities theory, we develop a theoretical framework positioning these capabilities as the core mediating mechanisms. Using panel data from Chinese manufacturing firms (2012–2023) and text mining with fixed-effects models, we find that AI significantly accelerates green transformation. Mechanism analysis confirms that dynamic capabilities mediate this relationship through three pathways: enhancing absorptive capability, stimulating innovation capability, and improving adaptive capability. Heterogeneity analysis reveals that the effect is stronger in non-state-owned, large-scale, and non-high-tech firms, suggesting that institutional and resource contexts shape AI’s impact. This article provides micro-level evidence on AI-driven green transformation in manufacturing enterprises from the dynamic capabilities perspective, offering theoretical and practical insights for advancing high-quality transformation of China’s manufacturing sector in the digital-intelligent era.
The results indicate that SMEs with stronger AI-driven capabilities and entrepreneurial competencies are more likely to develop higher strategic intelligence, which in turn enhances overall organizational performance.
Shrooq A. Alsenan, W. Al-rahmi, I. Alyoussef et al.· Frontiers in Artificial Inte...· 0 citations
This study investigates how AI adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions of business performance, and links digital transformation with sustainability outcomes.
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
Examining the impact of Artificial Intelligence adoption on the economic, environmental, and social dimensions of sustainable business performance among Indian MSMEs is expected to demonstrate that AI adoption significantly improves operational efficiency, innovation capability, environmental responsibility, and financial performance among MSMEs.
Dr.K. Surendran· International Journal of Dru...· 0 citations
The findings show that exploitative capability enhances both innovation capability and competitive advantage, while exploratory capability directly improves competitive advantage but does not significantly affect innovation capability.
L. Nguyen, Huong Thi Đinh, V. Tran et al.· International Journal of Asi...· 0 citations
This study develops and validates an artificial intelligence (AI) maturity construct grounded in dynamic capabilities theory, conceptualizing AI maturity as a reflective construct shaped by three antecedents: organizational readiness (OR), technological deployment (TD) and business model innovation (BMI). Rather than treating AI maturity as a static composite of its dimensions, this study conceptualizes it as an emergent capability that – viewed through a resource orchestration lens (Sirmon et al., 2011) – coordinates sensing, seizing and transforming activities and links them to multidimensional firm performance.
Survey data from 132 small- and medium-sized enterprises (SMEs) across multiple industries in South Korea are analyzed using partial least squares structural equation modeling (SmartPLS 4). AI maturity is operationalized as a reflective construct measured by dedicated items, with OR, TD and BMI specified as antecedent predictors. Digital transformation performance, firm age and firm size are included as control variables. Bootstrapping with 5,000 subsamples is employed for significance testing, and specific indirect effects are estimated via bias-corrected confidence intervals.
Business model innovation emerged as the strongest antecedent of AI maturity, followed by organizational readiness and technological deployment. AI maturity significantly enhances perceived AI-enabled performance outcomes – operational efficiency, market performance and financial stability. Specific indirect effects indicate full mediation through AI maturity for the organizational readiness and business model innovation pathways, whereas the technological deployment pathway is only marginally significant and is therefore classified as inconclusive.
The cross-sectional, single-country design limits causal inference and generalizability. Future research should employ longitudinal, multi-country designs with larger samples to validate the strategy-first antecedent pattern and test boundary conditions across diverse institutional contexts.
The BMI > OR > TD pattern of relative predictive strength is consistent with a strategy-first interpretation under dynamic capabilities theory; however, longitudinal designs are required to validate any temporal sequencing of investments. AI maturity serves as a diagnostic tool enabling managers to identify capability imbalances across the organizational, technological and business model dimensions.
This study advances AI maturity research by grounding AI maturity in dynamic capabilities microfoundations and providing empirical evidence of a strategy-first antecedent pattern in AI maturity formation. It resolves a theoretical misspecification in prior maturity models by treating dynamic capabilities theory as the primary mechanism and employing a separately measured reflective AI maturity construct.
Kwangwook Gang, Boreum Choi, G. Kim· Journal of Enterprise Inform...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
