The increasing complexity of contemporary service ecosystems has exposed significant limitations in existing Service Design Methods (SDMs), including methodological fragmentation, weak adaptability, limited lifecycle integration, and insufficient responsiveness to dynamic service environments. While Digital Twin (DT) technologies have been extensively applied in manufacturing and operational optimisation, their potential to support adaptive service design and the systemic evolution of SDMs remains underexplored. This study develops and validates a Digital Twin-supported systemic evolution framework for adaptive service ecosystems. Adopting a qualitative Design Science Research (DSR) approach, the study first synthesised the literature and relevant theoretical perspectives to develop a preliminary conceptual framework, which was subsequently validated through expert interviews involving 14 specialists from service design, digital transformation, and DT-related domains. Findings reveal that static procedural structures and inadequate ecosystem coordination capabilities constrain current SDMs. The results further demonstrate that DT technologies can function as adaptive infrastructures that enable real-time synchronisation, predictive analysis, continuous feedback integration, and lifecycle-oriented refinement. The literature synthesis led to the identification of four interconnected mechanisms: real-time adaptive feedback, service ecosystem synchronisation, predictive methodological evolution, and continuous lifecycle refinement, while expert evaluation confirmed their relevance, adaptability, and ecosystem-oriented value, highlighting implementation and scalability considerations. The study contributes theoretically by extending DT applications beyond operational optimisation toward methodological evolution, methodologically by integrating literature-driven framework development with expert validation, and practically by providing organisations with an adaptive framework for intelligent service ecosystem management and continuous service innovation.
Amangeldy, B., Imankulov, T., Tasmurzayev, N., Dikhanbayeva, G., & Nurakhov, Y. (2025). A review of artificial intelligence and deep learning approaches for resource management in smart buildings. Buildings, 15(15), 2631.
Antony, J., Sony, M., Lameijer, B., Bhat, S., Jayaraman, R., & Gutierrez, L. (2024). Towards a design science research (DSR) methodology for operational excellence (OPEX) initiatives. The TQM Journal, 36(8), 2383-2397.
Das, D. K. (2024). Exploring the symbiotic relationship between digital transformation, infrastructure, service delivery, and governance for smart sustainable cities. Smart Cities, 7(2), 806-835.
Duran, K., Cakir, L. V., Yigit, Y., Huseynov, K., Kusu, S. R., Ertürk, M. A., & Canberk, B. (2025). Toward Digital Twin-as-a-Service (DTaaS) Platforms: A Survey on Architecture, Design Requirements, and Performance Metrics. IEEE Communications Surveys & Tutorials, 28, 1845-1878.
Guisan, A., Chevalier, M., Adde, A., Zarzo?Arias, A., Goicolea, T., Broennimann, O., ... & Mateo, R. G. (2025). Spatially nested species distribution models (N?SDM): An effective tool to overcome niche truncation for more robust inference and projections. Journal of Ecology, 113(7), 1588-1605.
Herterich, M. M., Dremel, C., Wulf, J., & Vom Brocke, J. (2023). The emergence of smart service ecosystems—The role of socio?technical antecedents and affordances. Information Systems Journal, 33(3), 524-566.
Hoffmann, C., Avery, K. N., Macefield, R. C., Snelgrove, V., Rooshenas, L., Bekker, H. L., ... & McNair, A. G. (2025). Patient and surgeon perspectives of a large-scale system for automated, real-time monitoring and feedback of shared decision-making integrated into surgical practice: A qualitative study. BMJ Open, 15(6), e099090.
Jaakkola, E., Kaartemo, V., Siltaloppi, J., & Vargo, S. L. (2024). Advancing service-dominant logic with systems thinking. Journal of Business Research, 177, 114592.
Kilinc, T., Sjödin, D., & Parida, V. (2025). Navigating digital servitisation for the twin transition: how manufacturers can support customers with digitalisation and sustainability. Business Strategy and the Environment, 34(5), 5370-5385.
Lin, Y., Tang, J., Guo, J., Wu, S., & Li, Z. (2025). Advancing AI-enabled techniques in energy system modeling: a review of data-driven, mechanism-driven, and hybrid modeling approaches. Energies, 18(4), 845.
Mogaji, E. (2026). Reimagining transformative services in unregulated markets: conceptualising inclusive service provision in informal and developing country contexts. Journal of Services Marketing, 40(2), 188-200.
Mohanraj, R., & Balaji, S. N. (2026). Digital twin technology: A comprehensive review of modeling, applications, challenges and future directions in complex system integration. Archives of Computational Methods in Engineering, 33(3), 3291-3316.
Pan, Q., Zhou, L., Wang, Q., & Zhang, J. Z. (2025). The impact mechanism of value co-creation on manufacturing supply chain innovation ecosystems. Enterprise Information Systems, 19(7-8), 2524695.
Piras, G., Muzi, F., & Zylka, C. (2024). Integration of BIM and GIS for the Digitization of the Built Environment. Applied Sciences, 14(23), 11171.
Thanthrige, A., Lu, B., Sako, Z., & Wickramasinghe, N. (2025). Determinants of health care technology adoption using an integrated unified theory of acceptance and use of technology and task technology fit model: Systematic review and meta-analysis. Journal of Medical Internet Research, 27, e64524.
Torvinen, H., Komulainen, H., Nätti, S., & Saraniemi, S. (2026). Third time's the charm: revisiting the customer in public service logic. Public Management Review, 1-29.
Tripathi, N., Hietala, H., Xu, Y., & Liyanage, R. (2024). Stakeholders collaborations, challenges and emerging concepts in digital twin ecosystems. Information and Software Technology, 169, 107424.
Vafaei-Zadeh, A., Ong, J. Y., Hanifah, H., & Nikbin, D. (2026). Investigating factors influencing mobility-as-a-service (MaaS) adoption: an integrated technology acceptance model (TAM)-task-technology fit (TTF) perspective. International Journal of Urban Sciences, 1-30.
Ouyang, X., Yusof, M. J. M., & Perumal, T. (2026). Digital Twin-Supported Systemic Evolution of Service Design Methods: An Adaptive Framework for Complex Service Ecosystems. International Journal of Academic Research in Business and Social Sciences, 16(6), 937–949.
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