Human-Centered Challenges in IIoT-Enabled Manufacturing: A Qualitative Study of Worker–System Interaction in Digital Transformation

Abstract

The rapid advancement of digital technologies has accelerated the adoption of the Industrial Internet of Things (IIoT) in manufacturing, positioning digital transformation as a key driver of operational efficiency and innovation. However, the implementation of IIoT systems often overlooks the experiences and needs of frontline workers, resulting in misalignment between system design and actual work practices. This misalignment can negatively affect usability, adaptability, and overall system performance. This study investigates worker–system interaction in IIoT-enabled manufacturing from a human-centered perspective. Drawing on qualitative case studies conducted in traditional garment factories in Tongxiang, China, the research employs semi-structured interviews to examine workers’ experiences during digital transformation processes. The analysis focuses on how workers perceive, interpret, and engage with digital systems in real production contexts. The findings reveal that while IIoT technologies enhance production efficiency, their effectiveness critically depends on workers’ abilities to collect, understand, and utilize data within their workflows. Key challenges include mismatches between system functionalities and workers’ mental models, increased cognitive load, and limited support for skill adaptation. These issues highlight the importance of aligning system design with users’ cognitive and behavioral characteristics. This study contributes to the ergonomics and human-centered design literature by providing empirical insights into worker–system misalignment in IIoT contexts. It further proposes design implications for improving system usability and supporting sustainable digital transformation in manufacturing environments.