Exploring the drivers and barriers of digital twin adoption in Kenya and its implications for manufacturing asset optimisation

The fourth industrial revolution (I4.0) has accelerated the adoption of digital twin technology (DTT) as a system-level enabler of data-driven manufacturing transformation and intelligent asset management. While the literature increasingly recognises the potential of DTT, empirical research examining their adoption within the broader industrial ecosystem in African manufacturing contexts remains scarce. This study investigates DTT adoption in Kenya’s manufacturing sector by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) to develop a behavioural model situated within a nascent industrial ecosystem. Drawing on survey data collected from 257 senior managers and analysed using PLS-SEM, the study finds that DTT use is primarily influenced by managerial attitudes and perceived behavioural control, underscoring the importance of internal organisational readiness and capability-building over external normative pressures. The results also establish a positive relationship between DTT use and manufacturing asset optimisation, highlighting the ecosystemic value of digital transformation strategies in enhancing operational efficiency, predictive maintenance, and real-time decision-making. By advancing a socio-technical and ecosystem-informed perspective, this study offers a novel contribution to digitalisation scholarship in emerging economies and provides actionable insights for firms and policymakers seeking to embed DTT in system-wide industrial modernisation efforts.