National policies for science parks and innovation have been identified as one of the major driving forces for the innovation-driven economy, especially for publicly funded science parks. To investigate this collaborative ecosystem (government-academia-industry) for growth and sustainable development, this paper proposes a nation-wide economic impact analysis of science parks and innovation policy based on historical data drawn from one of the globally recognized high-technology industrial clusters in Taiwan. Systems thinking with causal loop analysis are adopted to improve our understanding of the collaborative ecosystem with science park policies. First, from a holistic viewpoint, the role of government in a science parks and innovation ecosystem is reviewed. A systems analysis of an innovation-driven economy with a science park policy is presented as a strategy map for policy implementers. Second, the added economic value and employment of the benchmarked science parks is evaluated from a long range perspective. Third, the concepts of government-academia-industry collaboration and policies to innovation ecosystem are introduced while addressing the measures and performance of innovation and applied R&D in the science parks. We conclude with a discussion of lessons learned and the policy implications of science park development and an innovation ecosystem.
PurposeThis paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.Design/methodology/approachThis paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.FindingsThe findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.Originality/valueIn the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.
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