The Application of Artificial Intelligence (AI) and Big Data in Developing and Optimizing Labor Norm Systems in Manufacturing Enterprises

Abstract

Amid the rapid pace of digital transformation in manufacturing enterprises, the adoption of Artificial Intelligence (AI) and Big Data is increasingly expected as a strategic approach to improving labor management efficiency. However, the mechanisms through which these technologies influence labor norm systems remain insufficiently clarified. This study seeks to examine how AI adoption, Big Data analytics capability, data quality, digital system integration, and human resource capability affect the effectiveness of labor norm systems, with data-driven decision-making capability serving as a mediating variable. A quantitative research design was employed, drawing on survey data collected from 350 manufacturing enterprises and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that technological and data-related factors positively contribute to strengthening data-driven decision-making capability. In turn, this capability exerts a significant impact on the effectiveness of labor norm systems. The results also confirm a partial mediating role of data-driven decision-making capability, although human resource capability does not demonstrate statistical significance in certain relationships. This study contributes to the literature by clarifying how technological investments are translated into organizational value through the development of internal capabilities. It also offers managerial implications, emphasizing the importance of synchronizing AI implementation, Big Data analytics, and decision-making competencies to enhance the performance of labor norm systems in manufacturing enterprises.