Inducement factor of talent agglomeration in the manufacturing industrial sector: A survey on the readiness of Industry 4.0 adoption

Abstract

China’s economy has progressed from a rapid growth phase to one of high-quality development and innovation. Industry 4.0 manufacturing technology and processes include cyber-physical systems (CPS), Industrial Internet of Things (IIOT), Cognitive Computing and Artificial Intelligence (CCAI) as advancements in computerization and information exchange the relevant variables data, and a survey questionnaire are used to accumulate three-year data from 2017 to 2019. The Structured Equation Modeling (SEM), analytic hierarchy process (AHP), and mediating variable in a SOBEL test are applied. The results show that Industry 4.0 is the primary practical corridor to official and familiar in sequence substitute policy and collaboration for talent agglomeration on research projects. It lowers the fixed price of human capital and significant factors active long-term innovation and profit at the end of the inferential test results. Hypotheses findings show that the associations between dependent and independent variables are essential, and latent variables GFI, CFI, TLI, and IFI have acceptable values. CMINDF and RMR fulfill the fit criteria and results will assist managers and policymakers in spotting talent agglomeration activities implemented to increase manufacturing businesses’ readiness to reap the most benefits from Industry 4.0 adoption.