Aug 2026· Journal of Cultural Analysis and Social Change· 0 citations· 39 references
TL;DR
It is asserted that policymakers and SME managers need to prioritise training, infrastructure and digital readiness to ease the path for AI adoption and the literature affirms that AI adoption increases the marketing performance of SMEs.
Abstract
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.
The study identifies 372 AI readiness factors and synthesises them into the TOP-L framework consisting of four dimensions and 20 factor clusters, thereby laying the foundation for a practical assessment approach, particularly suited for SMEs.
Maria Kretschmer, Alice Coen, R. Orth· European Conference on Knowl...· 0 citations
The findings emphasise that successful AI adoption depends more on organizational AI literacy for a firm than on technology investment, and suggest that policymakers should prioritise capability building initiatives, educators develop curricula on AI targeted towards specific job roles, and MSME leaders should focus on upskilling human resources before they consider acquiring any technologies.
Mohammad Falahat, Qi Yi Thong, Murali Raman et al.· Journal of Asian Scientific...· 0 citations
This study investigates the adoption of BI practices in small and medium-sized enterprises (SMEs) to examine the extent of BI integration and its impact on decision-making, competitiveness, and the critical success factors influencing effective BI implementation.
Mirano Jansen, K. Ohei, Sam Lubbe· Veredas do Direito· 0 citations
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.
Ridha Rayan Furqan, W. Adawiyah, Ali Şahin et al.· The International Conference...· 0 citations
Artificial intelligence (AI) has emerged as an important strategic tool for improving marketing performance and enhancing the competitiveness of small and medium-sized enterprises (SMEs). Despite its growing adoption, limited empirical evidence explains how different dimensions of organizational readiness influence AI usage in SMEs, particularly in emerging economies. This study investigates the effects of Technological Infrastructure Readiness (TIR), Human Resource Readiness (HRR), and Organizational Culture and Change Management Readiness (OCCMR) on AI Usage Behavior, with Behavioral Intention serving as a mediating variable. The proposed research framework integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology–Organization–Environment (TOE) framework, the Resource-Based View (RBV), and Organizational Readiness Theory. Data were collected from 402 SME owners and managers in Bangkok, Thailand, and analyzed using covariance-based structural equation modeling (CB-SEM). The measurement model demonstrated satisfactory reliability and validity, while the structural model exhibited an excellent fit (χ²/df = 1.91, CFI = .983, TLI = .978, RMSEA = .047). The results indicate that Technological Infrastructure Readiness, Human Resource Readiness, and Organizational Culture and Change Management Readiness significantly influence Behavioral Intention. In addition, Technological Infrastructure Readiness, Organizational Culture and Change Management Readiness, and Behavioral Intention have significant positive effects on AI Usage Behavior, whereas the direct effect of Human Resource Readiness on AI Usage Behavior is not significant. Behavioral Intention is the strongest predictor of AI Usage Behavior (β = .591, p < .001) and significantly mediates the relationships between organizational readiness and AI adoption.
Wanlop Aruntammanak, Yod Sukamongkol· Decision Science Letters· 0 citations
These findings highlight the necessity of customized implementation strategies, focused capacity-building initiatives, encouraging legislative frameworks, and multi-stakeholder cooperation to promote inclusive, responsible AI integration in SMEs.
E. S. Hamid, Bhenu Artha· Archives of Business Researc...· 1 citation
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