Scaling the low-altitude economy requires more than mature individual technologies. It also depends on interfirm knowledge collaboration between core-technology and scenario-application firms embedded in public governance. However, existing research rarely explains how dispersed technical and scenario data become usable knowledge for shared tasks or when interfirm collaboration can persist. The authors examine how verified data sharing shapes firms’ strategies through collaborative value added, collaboration frictions and data risks. The authors also analyse how benefit allocation, risk sharing and public governance tools affect collaborative evolution and its boundaries.
This study adopts a mixed-methods research design. Firstly, a tripartite evolutionary-game model involving the government, low-altitude core-technology firms and scenario-application firms is constructed to compare the relative payoffs of substantive collaboration and opportunistic collaboration under different governance conditions. MATLAB is then used to analyse equilibrium paths, policy-tool interactions and nonlinear data-risk boundaries. Secondly, questionnaire data from 333 managers of low-altitude enterprises provide complementary enterprise-level evidence concerning the direction and boundary conditions of the major variable relationships.
The model shows that the net effect of verified data sharing on collaborative strategies depends on the relative magnitudes of collaborative value added, friction savings, data risks and opportunistic gains. Bilateral substantive collaboration becomes self-sustaining only when benefit allocation and risk sharing give both firms positive net returns. Subsidies raise the relative payoff from substantive collaboration, whereas opportunism detection reduces the appeal of opportunistic strategies. Under our parameter settings, sustained, costly proactive governance is not stable once bilateral collaboration can persist under normal governance. Non-linear risk scenarios further show that additional risks may erode the benefits of greater sharing beyond the governance capacity assumed in the model scenarios. The empirical results also reveal a positive association between data sharing and collaborative innovation. This association is stronger when policy support and the corresponding dimension of market regulation are both high.
Firstly, this study distinguishes data flows from knowledge formation and integrates collaborative value added, risk sharing and governance tools within a dynamic framework centred on verified data sharing. Secondly, it conceptualises knowledge symbiosis as a bounded state of sustained bilateral collaboration and operationalises this state in the model as substantive collaboration in which both firms obtain positive net returns under normal governance. Finally, the study characterises the model-based value, risk and governance conditions under which such a collaborative state can emerge and remain locally stable, while clarifying the boundaries of data sharing and the distinct roles of supportive and restrictive governance tools.
