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Research on the impact of cognitive computing capabilities on enterprise dual innovation synergy: Dual perspectives of “Machine Intelligence” and “Human Intelligence”
Guo Yanlin, Chen Yantai, Sun Keke
Science Research Management ›› 2026, Vol. 47 ›› Issue (6) : 77-87.
PDF(1198 KB)
PDF(1198 KB)
Research on the impact of cognitive computing capabilities on enterprise dual innovation synergy: Dual perspectives of “Machine Intelligence” and “Human Intelligence”
With the rise of artificial intelligence-generated content (AIGC), AI has moved from decision-making 1.0 to generative 2.0. Machine intelligence (“machine intelligence”), exemplified by next-generation cognitive computing capabilities, demonstrated significant potential in generating both incremental solutions for iterative optimization and exploratory solutions for value reconstruction. However, whether innovative solutions can be adopted and achieve dual synergy still depends heavily on prudent decision-making by human intelligence (“human intelligence”), which is centered on the cognitive evaluation of the senior management team. In this context, integrating the perspectives of “machine” and “human” intelligence held significant theoretical value in analyzing the synergy mechanisms of enterprise dual innovation. This study developed a process model of “generation of dual innovation solutions - adoption - synergy realization of dual innovation”. Based on survey data from 343 enterprises in the Yangtze River Delta region that are implementing AI innovation, this model was empirically tested using hierarchical regressionand bootstrap methods. The results indicated that: (1) Cognitive computing capability has a significant positive impact on exploratory innovation, exploitative innovation, and dual innovation synergy, and has a stronger driving effect on exploratory innovation. (2) The relationship between cognitive computing capability and dual innovation synergy is only positively moderated by fit cognition, while the moderating effects of risk cognition and complexity cognition are not significant. (3) The three micro-processes of data-driven dynamic capabilities, namely, sense and response, integrated and utilization, and reconstruction and transformation, exhibit a chain-mediated impact on the relationship between cognitive computing capability and dual innovation synergy. The independent mediating effect of digital integration and utilization capability is insignificant. This study revealed the complementary boundaries of human-machine collaboration from the perspective of cognitive division of labor, deepened the theoretical mechanism of AI empowering digital innovation, and will provide practical insights for enterprises to optimize human-machine configuration and coordinate dual innovation with strategic decision-making.
cognitive computing capability / cognitive evaluation / dual innovation synergy / data-driven dynamic capabilities / machine intelligence / human intelligence
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