Aug 2026· The International Conference on Sustainable Economics Management and Accounting Proceeding· 0 citations· 66 references
TL;DR
It is demonstrated that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone.
Abstract
The relationship between artificial intelligence (AI) adoption and firm performance in small and medium-sized enterprises(SMEs) has attracted growing scholarly attention, yet the literature remains theoretically fragmented and dominated by techno-optimistic assumptions that portray AI as a direct pathway to superior performance. This study addresses two central research questions: why do AI adoption outcomes remain heterogeneous across SMEs, and through what organizational mechanisms does AI create business value? A systematic search of the Scopus database, followed by PRISMA-guided screening, yielded 29peer-reviewed articles analyzed through VOS viewer bibliometric mapping and qualitative content analysis. Four thematic clusters were identified: AI Adoption and SME Sustainable Performance; AI Investment and Firm-Level Heterogeneity; Green Intellectual Capital and Innovation; and AI Application and Service Productivity in China. Drawing on these clusters, an integrative Antecedents-Mediators-Moderators-Outcomes (AMMO) framework is proposed. Findings demonstrate that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone. Five value-creating mechanism categories are identified: innovation and transformation, human capital and knowledge, operational and productivity, strategic and behavioral, and financial and investment mechanisms. The study contributes by challenging techno-deterministic assumptions, advancing the AMMO framework as a conceptual architecture for future inquiry, and positioning green intellectual capital as an emerging theoretical frontier. Implications for managers, policymakers, and future researchers are discussed.
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...· 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
Artificial intelligence (AI) is increasingly presented as a route through which small and medium-sized enterprises (SMEs) can overcome information, capability and scale disadvantages. Yet adoption remains uneven, especially in emerging economies where finance, skills, infrastructure and institutional assurance are constrained. This systematic literature review investigates four questions: which technological, organisational and environmental conditions drive AI adoption; which barriers interrupt implementation; which business outcomes are reported; and how the international evidence can be translated into an actionable framework for Bangladesh. Following PRISMA 2020 principles, sixteen structured OpenAlex search routes retrieved 800 records. After removing 320 duplicates, 480 unique records were screened. A relevance pre-screen excluded 309 records; 171 database reports and 14 supplementary reports were assessed. Forty primary empirical studies were included, while eight reviews or conceptual sources were retained separately for theoretical triangulation. Study coding was organised through the Technology–Organization–Environment (TOE) framework and interpreted with resource-based and dynamic-capability perspectives. Human capital and employee skills were the most frequent driver (21 studies), followed by top-management support (12), ecosystem support (11), relative advantage (9) and digital/data readiness (9). The strongest explicit barriers concerned privacy, security, ethics and distrust (7), high cost and limited finance (6), institutional or regulatory gaps (5), and implementation complexity (4). Reported outcomes clustered around workforce learning (14), decision quality and agility (8), operational productivity (8), competitive advantage (8), revenue performance (7) and sustainability (6). However, nineteen studies relied on SEM or PLS-SEM, most evidence was cross-sectional, and only two primary studies directly examined Bangladesh. The paper therefore proposes a capability-gated pathway—diagnose, pilot, integrate, govern and scale—supported by Bangla-ready tools, shared training, affordable finance, vendor assurance and proportionate governance. The review concludes that AI produces business value not as a stand-alone purchase but when complementary skills, data routines, leadership and institutional supports are deliberately assembled.
Sanjida Binte Reza Chowdhury· Australian Journal of Artifi...· 0 citations
Artificial intelligence is rapidly transforming marketing by improving customer engagement and operational efficiency. Small and medium-sized enterprises (SMEs), especially in Qatar, are not able to adopt AI due to high adoption costs, poor technological skills and employee resistance. A thorough and methodological literature review was conducted to get a grasp on the factors influencing AI adoption and the subsequent effect on marketing performance. In accordance with the PRISMA guidelines, 32 peer-reviewed publications from 2020 to 2025 were analysed by applying a CASP/AMSTAR checklist, thematic coding and narrative analysis. The literature review has identified that technological readiness, managerial support, digital literacy and environmental factors are the major facilitators of AI adoption. However, context and organisational challenges are the main inhibitors of AI adoption. Nevertheless, the literature affirms that AI adoption increases the marketing performance of SMEs, and the TOE and DOI models are supported, while recommending a merger with the dynamic capabilities theory. Finally, the literature review asserts that policymakers and SME managers need to prioritise training, infrastructure and digital readiness to ease the path for AI adoption.
Salma El-Gohary, Mohamed Slim Ben Mimoun, Hatem El-Gohary· Journal of Cultural Analysis...· 0 citations
This study develops and validates an artificial intelligence (AI) maturity construct grounded in dynamic capabilities theory and 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
Positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China.
