基于DEMATEL-ISM的制造企业数字孪生技术采纳影响因素研究OACHSSCD

Digital twin technology,as a key technology to break through the core bottleneck of interaction and integration between the physical and information worlds in manufacturing,is an important means to achieve intelligent manufacturing.To deeply explore the influencing factors of digital twin technology adoption in manufacturing enterprises and reveal its internal mechanism and driving logic,a four-dimensional index system of technology,organization,environment,and economy is constructed based on literature analysis and expert interviews.The DEMATEL-ISM method is adopted to systematically analyze the causal relationship and hierarchical structure of the influencing factors,and ultimately 16 main influencing factors are identified.These factors are distributed in the four quadrants of the causal relationship coordinate,forming an 8-level hierarchical explanation structure model.The research results show that government incentive policies,data security and privacy protection,and digital twin maturity exhibit the highest influence degree and are the root factors of the system;industrial chain collaboration and cooperation and application benefits demonstrate the highest centrality,while intelligent infrastructure represents the lowest-level root factor.Factors in the technological and economic dimensions constitute the main influencing paths,while factors in the environmental dimension connect and act on the main paths,ultimately affecting the surface-level organizational dimension factors.Specifically,intelligent infrastructure,data security and privacy protection,and digital twin maturity,three factors in the technological dimension,are the key factors driving manufacturing enterprises to adopt digital twin technology.At the same time,government incentive policies,application benefits,and industrial chain collaboration and cooperation in the environmental,organizational,and economic dimensions play an important role in the decision-making process.Based on this,targeted suggestions are put forward,aiming to provide strong theoretical support and practical guidance for the digital transformation and intelligent upgrading of the manufacturing industry.