From Traditional TQM to AI-Based Quality Management: A Multiple Mediation Model of Digital Process Control and Service Performance

Abstract

In this paper, the author examines how the paradigm shift in Total Quality Management (TQM) to Artificial Intelligence-based Quality Management (AIQM) was changing. Industries are undergoing a high rate of digitization and this has altered the processes through which quality management can impact the organizational outcomes. The paper proposes and develops a multiple mediation model, where Digital Process Control and AI-improved monitoring are the intermediate variables between quality initiatives and the ultimate performance of the Service. In order to investigate these dynamics, we made use of an extensive dataset of 429 data instances that were obtained by various service-oriented businesses that are presently implementing smart technologies. The analysis framework was also built with the tools of structural equation modelling and path analysis, in particular, by using data science libraries and complex statistical software to perform mediation tests. It can be concluded that even though the traditional TQM principles are needed to give it a proper background, the implementation of AI-based tools can greatly increase the accuracy of the process regulation, which results in the increased reliability of the services delivered and customer satisfaction. The results offer a strategic plan for firms that wish to modernize their quality systems in the digital era.