A System Dynamics Model of Aerospace Data Asset Transactions: Simulating Technology–Market–Policy Co‐Evolution

ABSTRACT Against the backdrop of the rapid expansion of the digital economy, aerospace data asset transactions have emerged as a strategically consequential and systemically complex domain. Existing research has accumulated insights into five dimensions—technological innovation, market demand, policy support, data assets and enterprise performance—yet systematic modelling of their dynamic coupling mechanisms remains underexplored. Drawing on complex systems theory and synergy theory, this study integrated systematic literature review, semistructured expert interviews and case study evidence to identify key variables. A second‐order system dynamics model was subsequently constructed in Vensim 10.3.1 and used to simulate the co‐evolutionary process of the five subsystems over 2018–2035, with 2018–2024 data serving for model calibration and 2025–2035 outputs representing simulation projections. Four findings emerge from the analysis. First, the five subsystems exhibit a phased co‐evolutionary pattern characterised as technology‐led, market‐following, system‐converging: The compound annual growth rate (CAGR) of digital technology innovation (20.17%) consistently outpaced both aerospace data demand (7.92%) and policy support (6.87%), generating endogenous structural tension and a persistent growth differential between technology innovation and enterprise performance (CAGR = 7.79%). Second, the influence of technological innovation on enterprise performance is subject to an approximately 4‐year lag effect, attributable to the role of R&D accumulation as an intermediate buffer stock. Third, policy support exhibits a threshold effect: Once the policy support stock crosses a critical level, its stabilising effect on market demand becomes increasingly pronounced. Fourth, a structural imbalance exists between data asset creation (CAGR = 0.67%) and technological innovation (CAGR = 20.17%), reflecting a competitive constraint in resource allocation. These findings extend the explanatory dimension of data asset value creation from static factor analysis to dynamic coupling mechanisms and offer a quantitative basis for both enterprise strategic planning and policy design.