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.
Introduction This study examines the effects of Artificial Intelligence (AI) capabilities and entrepreneurial competency on Small and Medium Enterprise (SME) performance through the mediating role of strategic intelligence in SMEs operating in Malaysia and Saudi Arabia. Grounded in Resource Orchestration Theory and Core Competency Development Theory, the study explores how AI integration skills, smart decision-making abilities, innovativeness, and risk management competencies contribute to enhanced organizational performance in an increasingly digital business environment. Methods A cross-sectional quantitative research design was adopted. Data were collected from 320 SMEs in Malaysia and Saudi Arabia using a structured questionnaire. The measurement model and structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study examined direct relationships between AI capabilities, entrepreneurial competency, strategic intelligence, and SME performance, as well as the mediating effect of strategic intelligence. Results The findings reveal that both AI capabilities and entrepreneurial competency have a significant positive impact on strategic intelligence and SME performance. Furthermore, strategic intelligence was found to significantly mediate the relationship between AI capabilities, entrepreneurial competency, and SME performance. These 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. Discussion The study contributes to the growing body of literature on AI-driven organizational capabilities by providing empirical evidence from developing economies, specifically Malaysia and Saudi Arabia. The results highlight the importance of integrating AI competencies with entrepreneurial skill development to improve strategic decision-making and business competitiveness. Practically, the findings suggest that SME managers and policymakers should prioritize AI adoption and entrepreneurial capability development as key strategies for improving innovation, resilience, and long-term performance in SMEs.
Shrooq A. Alsenan, W. Al-rahmi, I. Alyoussef et al.· Frontiers in Artificial Inte...· 0 citations
The rapid advancement of Artificial Intelligence (AI) technologies has transformed the way organizations operate,
innovate, and create value in an increasingly competitive business environment. AI adoption enables firms to automate
processes, enhance decision-making, improve operational efficiency, and deliver personalized customer experiences. However,
the extent to which AI contributes to sustainable business performance depends not only on technological implementation but
also on an organization's ability to innovate. This study examines the relationship between Artificial Intelligence adoption and
sustainable business performance, with innovation capability serving as a mediating variable. Specifically, it investigates how AI
adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions
of business performance. The study adopts a quantitative research approach using structured questionnaires administered to
managers and professionals from manufacturing and service organizations. Data will be analyzed using Structural Equation
Modeling (SEM) to examine the direct and indirect relationships among the study variables. The proposed conceptual
framework posits that AI adoption positively influences innovation capability, which subsequently enhances sustainable business
performance. The findings are expected to provide empirical evidence on the strategic role of AI in fostering innovation and
achieving long-term organizational sustainability. Furthermore, the study offers practical implications for business leaders,
policymakers, and practitioners by highlighting the importance of integrating AI technologies with innovation-driven strategies
to improve competitiveness and sustainable growth. By linking digital transformation with sustainability outcomes, this research
contributes to the growing body of knowledge on AI-enabled business innovation and supports the achievement of the United
Nations Sustainable Development Goals, particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry,
Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production).
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
Artificial Intelligence (AI) has emerged as a transformative technology that enables organizations to enhance operational
efficiency, optimize resource utilization, improve customer engagement, and support sustainable business practices. For
Micro, Small, and Medium Enterprises (MSMEs), AI adoption presents significant opportunities to overcome resource
constraints, improve competitiveness, and achieve long-term sustainability. Despite increasing government initiatives
promoting digital transformation in India, empirical evidence regarding the relationship between AI adoption and
sustainable business performance among Indian MSMEs remains limited. This study aims to examine the impact of
Artificial Intelligence adoption on the economic, environmental, and social dimensions of sustainable business
performance among Indian MSMEs.
The study adopts a quantitative research approach using a structured questionnaire administered to MSME owners,
managers, and entrepreneurs across selected industrial clusters in India. Primary data are collected from 400 respondents
through convenience and purposive sampling techniques. Statistical tools such as Descriptive Statistics, Reliability
Analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modeling
(SEM), and Multiple Regression Analysis are employed to test the proposed hypotheses. The study considers AI adoption
as the independent construct comprising intelligent automation, predictive analytics, customer relationship management,
decision support systems, and process optimization, while sustainable business performance is measured through
economic sustainability, environmental sustainability, and social sustainability indicators. The findings are expected to
demonstrate that AI adoption significantly improves operational efficiency, innovation capability, environmental
responsibility, and financial performance among MSMEs. The study also highlights the role of organizational readiness
and digital capabilities in facilitating successful AI implementation. The research contributes to the growing literature on
AI-enabled sustainability by providing empirical evidence from the Indian MSME sector and offers practical
recommendations for policymakers, entrepreneurs, technology providers, and researchers. The outcomes of this study are
expected to support India's vision of digital transformation, sustainable industrial development, and enhanced global
competitiveness of MSMEs
Dr.K. Surendran· International Journal of Dru...· 0 citations
This study examines how dynamic capabilities and artificial intelligence (AI) capabilities shape innovation, competitive advantage, and business performance (BP) in Vietnamese small and medium-sized enterprises (SMEs). Drawing on the dynamic capabilities view, survey data from 1,512 managers in 301 SMEs were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). 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. Innovation capability positively influences BP both directly and indirectly through competitive advantage. AI capability emerges as a key organizational capability: it strongly predicts innovation capability, improves BP, and strengthens the relationship between innovation capability and competitive advantage. The study contributes by distinguishing between exploitation and exploration, positioning AI capability beyond technology adoption, and demonstrating how AI helps SMEs convert innovation into competitive advantage.
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.
