Xuebin Huang (Author)
Guangzhou College of Technology and Business, Guangdong 510800, Guangzhou province, China
Jianwen Shen (Primary Contact)
Guangzhou City University of Technology, Guangzhou province, China
Biao Zhang (Author)
Guangzhou College of Technology and Business, Guangdong 510800, Guangzhou province, China
Qianyi Chen (Author)
Guangzhou College of Technology and Business, Guangdong 510800, Guangzhou province, China
Hao Li (Author)
Guangzhou College of Technology and Business, Guangdong 510800, Guangzhou province, China
Artificial Intelligence, Human-Machine Collaborative Innovation, Digital Capabilities, HRM Transformation, Mediating Effect, Moderating Effect
Interim Results of the Project (BDPG25142)By Guangdong Educational Evaluation Association, .Interim Results of the Project (GDZLGL25037)By Guangdong Association for Higher Education Teaching Administration.
31-12-2025
To address the triple mismatch dilemma of "technology application-capability requirements-HRM
system" in AI-driven human-machine collaborative innovation, this paper, based on the perspective of HRM transformation, employs SEM and cross-case comparison methods to reveal its efficacy mechanism, drawing on publicly available data from 326 multi-industry enterprises and 2024 case studies of 12 benchmark enterprises. The study finds that AI positively influences innovation efficacy through three-dimensional mechanisms of "task reconfiguration, information interaction, and decision complementarity," with the decision complementarity effect being the strongest (β=0.35, p<0.001). Employees' three-tier capabilities of "basic operations-digital cognition-innovative application" fully mediate this relationship (total mediating effect =0.29, 95%CI=[0.21,0.37]), with innovative application capability contributing the most (51.7%); The three-dimensional mechanisms of HRM transformation differentially moderate this process-capability diagnosis moderates digital cognition (β=0.18, p<0.01), hierarchical training moderates innovative application (β=0.21, p<0.001), and institutional safeguards moderate basic operations (β=0.16, p<0.01). Industry differences manifest as manufacturing focusing on basic operations and task reconfiguration, while service industry emphasizing digital cognition and decision complementarity. This paper constructs a three-dimensional framework of "AI technology - digital capabilities-HRM system," providing theoretical support for enterprise HRM transformation, increasing the success rate of human-machine collaborative innovation by 35%, and offering practical pathways for multiple industries to overcome technology implementation challenges.
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