This study aims to systematically map and synthesise the fragmented knowledge on digital twins (DTs) within tourism destination management. It also highlights how DTs can transform destination governance, sustainability practices, crisis management and visitor engagement through real-time simulation and data-driven insights.
Following the PRISMA protocol, peer-reviewed studies were systematically collected from three databases (Web of Science, Scopus, ScienceDirect) and analysed using BERTopic, a BERT-based machine learning algorithm for topic modelling. This machine learning based computational literature review approach enabled the identification of hidden thematic patterns across interdisciplinary sources.
The analysis revealed four dominant themes: digital transformation and heritage management, crisis management and adaptive resilience, data-driven visitor engagement and sentiment analysis and immersive futures with the metaverse. These themes were synthesised into a novel socio-technical maturity framework that positions DT adoption along two continua: technical complexity (static representation to dynamic simulation) and social integration (centralised control to collaborative governance), thus revealing four developmental levels and their inherent tensions: ownership versus access, automation versus human judgment, personalisation versus surveillance and virtual versus authentic.
The framework provides DMOs and policymakers with a diagnostic tool to assess their DT maturity, anticipate socio-technical challenges at each level, and strategically guide digital transformation, crisis resilience and visitor engagement.
To the best of the authors’ knowledge, this study pioneers machine learning-driven topic modelling in tourism DT research, offering the first socio-technical framework that conceptualises DTs not merely as technological artefacts but as socio-technical systems, reframing destination management from reactive administration to predictive governance, from physical places to phygital ecosystems and from technology adoption to socio-technical orchestration.
