Jul 2026· Journal of Asian Scientific Research· 0 citations· 55 references
TL;DR
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.
Abstract
AI adoption by Micro, Small, and Medium-sized Enterprises (MSMEs) in Malaysia remains low despite the wide availability of artificial intelligence (AI) tools, as well as government support initiatives. This study investigates barriers, enablers, and outcomes of AI adoption with emphasis on human and organizational factors. Using a qualitative research method, this study conducted interviews with decision makers from manufacturing, technology, and professional services to get insights on AI adoption. The results reveal that a low level of AI adoption among MSMEs is primarily a result of cost and technology barriers, showing instead that people, knowledge, and organizational culture deficits are more prominent contributors to the findings. Alternatively, AI illiterate (in terms of conceptual knowledge, lack of strategic vision, prompting capabilities) is the ultimate block. As for key enablers, great contributing factors are targeted training initiatives, the presence of a top management “AI-first” mindset, and the use of incremental learning pathways. Nonetheless, inconsistencies in policy implementation and limited trust in external vendors weaken institutional support mechanisms. 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.
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
Background: The adoption of Business Intelligence (BI) practices has gained attention across enterprises as rapid advances in digital technologies enable the automation of business processes and enhance organisational competitiveness. BI tools offer SMEs the ability to generate insights that support informed decision-making. Despite these benefits, SMEs often encounter challenges in adopting and implementing BI, particularly in relation to financial constraints, limited staff skills, and organisational culture. Objectives: 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. Method: A qualitative research approach aligned with an interpretivist paradigm was employed. The Technology Acceptance Model (TAM) was adopted as the lens to guide the study. Data were collected through in-depth interviews with nine (9) participants, including owners, a director and co-founder, and employees from three purposively selected SMEs. Participants were chosen to reflect diverse organisational roles and experiences with BI tools. The interview data were analysed thematically to identify patterns related to BI adoption, utilisation, and effectiveness. Results: The findings indicate that BI adoption among South African SMEs remains limited, with many enterprises relying primarily on basic BI tools. Nevertheless, SMEs that have adopted BI reported improvements in decision-making speed and overall competitiveness. Key challenges identified include data integration issues, financial limitations, and a lack of technical skills. Successful BI adoption was found to depend on leadership support, appropriate training, strategic alignment, and the use of tailored, cloud-based BI solutions. Conclusion: The study concludes that while BI adoption offers notable performance benefits for South African SMEs, its success is contingent upon addressing financial, skills-related, and cultural barriers. Enhanced funding support, practical training initiatives, and effective change management are essential. The study contributes practical insights into the enablers and challenges of BI adoption in a developing economy context and extends the discourse on BI adoption in emerging economies, offering guidance for policymakers, SME owners, and technology providers seeking to enhance SME competitiveness through data-driven decision-making.
Mirano Jansen, K. Ohei, Sam Lubbe· Veredas do Direito· 0 citations
Grounded in the Technology Acceptance Model (TAM), this study investigates what determines AI adoption behaviour and user satisfaction among staff and students in Malaysian and Indonesian universities. Survey data were gathered from 748 respondents across 12 institutions in 2025. Using binary logistic regression to identify predictors of training participation and k-means clustering to reveal latent user segments, we find that perceived usefulness and perceived ease of use are stronger predictors of both AI usage and satisfaction than formal training attendance. Malaysian respondents were 2.47 times more likely to have completed AI training than their Indonesian counterparts (p < .001), yet this structural advantage produced no significant difference in satisfaction between the two countries (Mann-Whitney U, p = .214). Three user profiles emerged: AI Skeptics (23.8%), who require demonstration of practical task value before any training engagement; AI Learners (42.9%), who benefit most from discipline-embedded mentoring and competency recognition; and AI Champions (33.3%), best deployed as peer facilitators rather than additional training recipients. For policymakers, these findings indicate that satisfaction-focused AI strategy must prioritise ease-of-use improvements and workflow integration over training volume, while Indonesian institutions specifically need structural investment in infrastructure and governance frameworks as preconditions for effective capacity-building.
Mohd. Azlishah Othman, A. Jaafar, Redzuan Abd Manap et al.· International journal of res...· 0 citations
Despite the great potential, many small and medium-sized enterprises (SMEs) still hesitate to use artificial intelligence (AI). One key reason is the concern about acceptance by employees. To gain a deeper understanding of the influencing factors and their relationship with AI experience in SMEs, a survey and a structural equation model (SEM) were developed. Thereby, the study explicitly focused on operational employees. The findings show that over 62% of the variance in employee acceptance of AI can be explained by six key determinants: usefulness, ease of use, job insecurity, productivity, environmental sustainability, and business culture. Among these factors, business culture exerts the strongest correlation on AI acceptance. Environmental sustainability also plays a more substantial role than ease of use and job insecurity. These results underscore the relevance of a company’s own business culture and the perceived ecological impact of AI applications. The findings offer valuable insights for entrepreneurs seeking to foster greater employee acceptance. Additionally, the study reveals a significant negative correlation between AI experience and its perceived ease of use. This suggests that companies should prioritize user-friendliness more strongly when selecting AI applications, and it underscores the need for training and time for familiarization. Overall, the findings contribute to a deeper understanding of successful AI implementations, thereby supporting the greater spread of AI in SMEs.
Leon Oldemeyer, Andreas Jede, Frank Teuteberg· Journal of Small Business St...· 0 citations
The digitising of local authorities (LAs) is increasingly perceived as an essential solution for improving service delivery, transparency, and administrative efficiency. Successful digital adoption depends not only on technological capabilities but also on organisational and human factors. Non-technical factors such as organisational culture, team dynamics, and management support have been shown to play a critical role in this process. This research utilized a nonexperimental cross-sectional quantitative design that examined the survey data of 30 respondents, with one purposively selected knowledgeable respondent from each of 30 LAs in four states of Malaysia, and the data were analysed descriptively and correlatively. Results suggest that team culture received the highest mean rating among the non-technical factors, and that organisational structure, management support, organisational culture, and individual skills play supportive but secondary roles. Intermediate-adoption characteristics received the highest mean rating, followed by future readiness, basic adoption, and advanced adoption. Organisational and human factors were associated with different dimensions of digital adoption, particularly advanced adoption and future readiness. The implications indicate that digital transformation in LAs is essentially an organisational challenge; this implies the need for leadership-driven shaping of digitised authorities, which focus on people and are applied uniformly over all areas of LAs.
Unknown authors· Journal of Governance and Re...· 0 citations
Small and Medium-sized Enterprises (SMEs) in Hong Kong face intense pressure to adopt Artificial Intelligence (AI) for business development, yet many fail to achieve anticipated outcomes. This study proposes and theoretically validates a readiness-based model explaining AI adoption pathways. Grounded in the Resource-Based View (RBV), the model posits that three independent variables—Talent Readiness, Financial Access, and Change Management—influence the dependent variable of Expected Outcomes in Business Development. Using an explanatory-correlational quantitative design with a target sample of 150 Hong Kong SMEs, this study details how Pearson and partial correlation analyses can be used to test the proposed relationships. Power analysis confirms that N=150 is sufficient to detect SMEs at α = 0.05. The study delineates distinct adoption pathways (transformational, incremental, futile, and stagnant) and assesses the SME's effectiveness in market expansion, revenue growth, and process innovation. The research indicates that SMEs in Hong Kong are recommended to adopt a stepwise approach. This approach involves first enhancing talent and change management capabilities, followed by securing funding, and finally implementing advanced AI. This readiness-first strategy is intended to position AI as a catalyst for business growth rather than a financial liability.
Jacky Mong, Kwan Watt· The International Journal of...· 0 citations
