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.
Mohammad Falahat, Qi Yi Thong, Murali Raman et al.· Journal of Asian Scientific...· 0 citations
This study examines how AI adoption enablers influence AI-driven innovation, competitiveness and multidimensional firm performance among micro, small and medium-sized enterprises (MSMEs).
Data were collected from 268 Malaysian MSMEs and analysed using partial least squares structural equation modelling. A disjoint two-stage approach was used to model firm performance as a higher-order construct comprising technological, economic and sustainability performance. Necessary condition analysis (NCA) and importance-performance map analysis were also applied to identify performance bottlenecks and managerial priorities.
The results show that environmental conditions and technological readiness are positively and significantly associated with AI adoption, whereas organisational support does not operate as a sufficient adoption driver. However, AI adoption is strongly associated with AI-driven innovation, which in turn strengthens competitiveness and improves firm performance. The NCA further shows that high firm performance requires minimum threshold levels of technological readiness, environmental conditions, organisational support, AI adoption, AI-driven innovation and competitiveness.
Managers should not treat AI adoption as a symbolic digital upgrade. Performance gains depend on converting AI into process, product/service and business model innovation, and then into competitiveness. Policymakers should move beyond isolated AI grants towards coordinated support systems that combine infrastructure, skills development, advisory services, innovation financing and ecosystem confidence.
The study develops the Technology–Environment–Capability for AI Performance (TEC-AIP) Framework, integrating sufficiency and necessity logics to explain AI-enabled MSME performance in an emerging economy.
Mohammad Falahat, R. Thurasamy, Pureheart Ogheneogaga Irikefe et al.· Benchmarking : An Internatio...· 0 citations