Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organiz...Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organizational knowledge diffusion.First,we develop a knowledge diffusion model based on continuous human–AI interactions,and we use the Agent-Based Modeling (ABM) method to simulate the diffusion process within the collaborative team and assess diffusion rates and efficiency based on knowledge levels.Second,we examine the interdependencies among members under different roles of AI,integrating AI cognitive capabilities,human–AI cognitive trust,and task interdependencies,and build a tie strength measurement model from the Social Network Analysis (SNA) perspective.Third,an entropy-based model is introduced to measure AI’s cognitive capability,accounting for project complexity and AI-generated solution uncertainty.We also establish a dynamic cognitive trust model that incorporates both the dynamic nature of trust in human–AI interactions and AI’s cognitive capability.Task interdependencies are assessed through a multi-dimensional activity network,and visualized by the Dependency Structure Matrix (DSM) method.Finally,an industrial example is provided to demonstrate the proposed model.Results show that organizational knowledge diffusion performs best when AI acts both as a collaborator and a tool.Moreover,this paper provides new insights,including how trust and task interdependencies significantly impact knowledge diffusion in human–AI collaborative organizations.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.W2441021 and 72271022).
文摘Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organizational knowledge diffusion.First,we develop a knowledge diffusion model based on continuous human–AI interactions,and we use the Agent-Based Modeling (ABM) method to simulate the diffusion process within the collaborative team and assess diffusion rates and efficiency based on knowledge levels.Second,we examine the interdependencies among members under different roles of AI,integrating AI cognitive capabilities,human–AI cognitive trust,and task interdependencies,and build a tie strength measurement model from the Social Network Analysis (SNA) perspective.Third,an entropy-based model is introduced to measure AI’s cognitive capability,accounting for project complexity and AI-generated solution uncertainty.We also establish a dynamic cognitive trust model that incorporates both the dynamic nature of trust in human–AI interactions and AI’s cognitive capability.Task interdependencies are assessed through a multi-dimensional activity network,and visualized by the Dependency Structure Matrix (DSM) method.Finally,an industrial example is provided to demonstrate the proposed model.Results show that organizational knowledge diffusion performs best when AI acts both as a collaborator and a tool.Moreover,this paper provides new insights,including how trust and task interdependencies significantly impact knowledge diffusion in human–AI collaborative organizations.
