2026· International journal of research and innovation in social science· 0 citations
TL;DR
An extended theoretical framework that integrates AI-specific trustworthiness and organizational digital maturity as contingency mechanisms is proposed and offers actionable directives for policymakers and business managers seeking to accelerate digital maturity.
Abstract
Artificial Intelligence (AI) technology has transitioned from an advanced corporate application into an accessible operational utility for contemporary business management. For Small and Medium Enterprises (SMEs), integrating AI capabilities offers substantial potential to optimize internal workflows, enhance decision-making precision, and strengthen market positioning. However, actual AI adoption among smaller enterprises in developing economies remains fragmented due to resource limitations and technical risks. Moving beyond a conventional application of the Technology-Organization-Environment (TOE) framework, this paper proposes an extended theoretical framework that integrates AI-specific trustworthiness and organizational digital maturity as contingency mechanisms. The conceptual model evaluates primary drivers across technological (relative advantage, complexity), organizational (top management support, financial readiness), and environmental contexts (competitive pressure, regulatory support), while incorporating Perceived AI Trustworthiness as a mediating mechanism and Organizational Digital Maturity as a moderating condition. The paper formulates eight testable propositions and details a quantitative methodology based on Partial Least Squares Structural Equation Modeling (PLS-SEM) for a target sample of N=300 SMEs. This framework advances theoretical understandings of technology adoption in emerging economies and offers actionable directives for policymakers and business managers seeking to accelerate digital maturity.
The findings suggest that AI creates enterprise value through cognitive automation, decision intelligence, and business model innovation, but their effectiveness depends on data governance, digital leadership, human capital, financial readiness, and regulatory support.
Diep Van Vu· Tạp chí Khoa học Đại học Côn...· 0 citations
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 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
The empirical and conceptual research on artificial intelligence (AI) adoption in small and medium-sized enterprises (SMEs) that was published between 2019 and 2025 is compiled in this systematic literature review. This evaluation incorporates information from three main research domains: AI applications in business operations, adoption enablers and barriers, and organizational performance results. It is based on fifty peer-reviewed articles that were found through searches of Scopus, Web of Science, and IEEE Xplore. Results show that in order to improve customer service, marketing automation, and operational efficiency, SMEs primarily use machine learning, natural language processing, and chatbot technologies. While financial limitations, a lack of skills, and insufficient digital infrastructure continue to be obstacles, top management support, technical preparedness, and staff capability emerge as crucial organizational facilitators. Although adoption rates are still concentrated among larger organizations, AI adoption is correlated with quantifiable increases in operational efficiency, cost reduction, customer retention, and innovation performance. There are clear regional differences, with rising nations in Latin America and Asia spearheading adoption despite more limited resources. Significant research gaps about long-term sustainability, contextual differences among developing economies, ethical governance frameworks, and the efficacy of support interventions are identified by the review. To promote inclusive, responsible AI integration in SMEs, our findings highlight the necessity of customized implementation strategies, focused capacity-building initiatives, encouraging legislative frameworks, and multi-stakeholder cooperation. For practitioners, governments, and researchers looking to close the gap in AI adoption and uncover revolutionary potential for small businesses worldwide, this paper offers practical ideas.
E. S. Hamid, Bhenu Artha· Archives of Business Researc...· 1 citation
The authors present a conceptual framework for the relationship between AI capability inputs and organizational absorption processes and downstream effects, which are moderated by the regulatory environment and innovation industry context and mediated by innovation capability and employee AI literacy.
Zarin Subha Progga, Mohammad Ali· Journal of business and mana...· 0 citations
Small manufacturing enterprises remain economically important but continue to face recurring operational constraints in planning, scheduling, quality control, maintenance, and process-data use. Generative artificial intelligence offers increasingly accessible support for these bounded operational tasks, yet adoption remains uneven because many firms lack a coherent basis for linking digital opportunity to internal resources, organizational knowledge, and measurable operational improvement. This study develops a conceptual framework that integrates the resource-based view and the knowledge-based view to explain generative artificial intelligence adoption in small manufacturing enterprises. Using an evidence-grounded theory-development approach, the study builds a staged framework that separates foundational conditions, perceived operational AI opportunity, organizational translation mechanisms, and performance outcomes. The framework theorizes internal resources and knowledge assets as foundational antecedents, perceived generative artificial intelligence potential in operational functions as the adoption bridge, knowledge integration and dynamic capability as organizing mechanisms, and performance improvement as the downstream consequence. It further explains how conceptual clarity can support later empirical reduction without losing the richer logic needed for practical implementation. The study also clarifies how the framework can guide applied information-system design through data-readiness assessment, bounded decision-support use cases, human-in-the-loop verification, and operational KPI monitoring. The resulting architecture strengthens theoretical explanation and operational design logic for generative artificial intelligence adoption in constrained manufacturing environments, while preserving clear boundaries for subsequent validation and applied deployment.
Ikhwan Arief, Alizar Hasan, N. T. Putri et al.· Jurnal RESTI (Rekayasa Siste...· 0 citations
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