Digital innovation ecosystems operating in Industry 4.0 environments increasingly encounter resistance and systemic instability driven not by technological limitations but by user adaptation stress under conditions of rapid change. Although Quadruple Helix governance models structure institutional collaboration among academia, industry, government, and civil society, they do not incorporate empirically validated human adaptation parameters into ecosystem design. The aim of this study is to develop and empirically ground a four-level adaptive governance architecture integrating psychophysiological user profiling into digital ecosystem management. The research is based on a multi-stage empirical study (N = 479; EEG subsample n = 50) combining psychophysiological measurement and subjective state assessment. Latent Profile Analysis identified four stable adaptation profiles (entropy = 0.93). Experimental findings demonstrate a significant stress amplification effect under conditions of novelty combined with time pressure, reflected in increased mental workload and reduced relaxation levels. The proposed architecture translates adaptation profiles into operational governance parameters across four layers: operational interaction modulation, innovation rollout sequencing, conceptual governance integration, and socio-cultural stabilization. The framework enables integration with wearable stress indicators, behavioral analytics, and AI-driven interface adaptation. The results provide both theoretical advancement by bridging behavioral adaptation research with digital ecosystem governance and practical value through a scalable decision-support model for adaptive innovation management.