Enhancing Flexible Healthcare Management Through the Adoption of Digital Twin Technology: An Integrated UTAUT2-TOE Framework with SEM-ANN Analysis

Abstract This study examines the behavioral and organizational factors that influence the adoption of digital twin (DT) technologies in healthcare, particularly in resource-constrained environments such as Bangladesh. It emphasizes the often-overlooked human and institutional aspects of DT adoption, alongside technical considerations. An integrated model was developed by combining the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the Technology-Organization-Environment (TOE) framework, drawing on six key constructs from each. Data collected from 439 healthcare professionals were analyzed using a hybrid approach of structural equation modeling (SEM) and artificial neural networks (ANNs). The results show that all predictors have a significant impact on behavioral intention, with complexity having a negative effect. ANN sensitivity analysis identified regulatory support, effort expectancy, facilitating conditions, and complexity as the most influential factors. These contribute to flexible management dimensions in hospitals. While the model explains 88.4% of the variance in behavioral intention, it accounts for only 8.5% of actual DT adoption, suggesting the influence of other organizational factors. This study is among the first to use an integrated UTAUT2-TOE framework with an SEM-ANN approach for DT adoption in healthcare. The findings provide valuable insights for healthcare policymakers, underscoring the importance of supportive infrastructure, user-friendly technology, and regulatory alignment.