The accelerating digital economy demands service systems that are adaptive, data-driven, and collaborative. This study introduces an integrated stakeholder-driven service innovation framework that combines system dynamics modeling, the fuzzy analytic hierarchy process (FAHP), and a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model to enhance organizational responsiveness and decision intelligence. Drawing on global datasets from the World Economic Forum and OECD (2020–2025) and validated through multi-country case studies in Germany, China, and Singapore, the framework models complex stakeholder interactions, prioritizes uncertain preferences, and forecasts digital-skill demands with high accuracy. The FAHP component achieves a consistency ratio of 0.07 in weighting stakeholder priorities, while the CNN-LSTM model predicts emerging competencies with 93.4% accuracy (RMSE = 0.11). Empirical evidence from 1,800 stakeholders across 15 institutions shows measurable improvements in service relevance (+17.8%), outcome effectiveness (+12.6%), and overall stakeholder satisfaction. Compared with conventional static approaches, the framework demonstrates greater agility in adapting to digital disruptions such as artificial intelligence integration and green transformation. By linking stakeholder collaboration with intelligent analytics, this research offers a scalable strategy for service excellence and sustainable innovation in the global digital economy, contributing to both informatics-based management and service science theory. © 2025, Success Culture Press. All rights reserved.
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