The increasing complexity of digitally transformed service ecosystems has challenged the effectiveness of conventional Service Design Methods (SDMs). Existing SDMs are frequently characterised by methodological fragmentation, limited adaptability, weak lifecycle integration, and insufficient responsiveness to dynamic stakeholder interactions. While Digital Twin (DT) technology has gained significant attention in manufacturing and engineering contexts, its potential contribution to service design evolution remains underexplored. This study investigates how DT integration can support the systemic evolution of SDMs within contemporary service ecosystems. Adopting a Design Science Research approach, the study develops a conceptual framework that combines methodological structuring through the MO Mapping Framework, evolutionary trend analysis, and DT-enabled adaptive mechanisms. Findings indicate that DT capabilities—including real-time monitoring, predictive analytics, continuous feedback integration, and ecosystem coordination—can significantly enhance the adaptability and responsiveness of service design practices. The proposed framework demonstrates how DT technologies can facilitate continuous methodological refinement and intelligent service innovation across complex socio-technical environments. The study contributes to service management literature by extending DT applications beyond operational optimisation and introducing a novel perspective on adaptive service methodology evolution. Practical implications are provided for organisations seeking to strengthen digital transformation initiatives and improve service innovation capabilities through data-driven and ecosystem-oriented approaches.
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