Driving Factors and Transmission Mechanisms of Quality Risks in Digitalized Equipment Manufacturing Processes: A Mixed-Methods Study Integrating Grounded Theory, SEM, and XGBoost-SHAP

Quality risks in digitalized equipment manufacturing processes are increasingly characterized by multi-source coupling, chain-like transmission, and latent evolution, making it difficult for single-method approaches alone to fully reveal their transmission mechanisms and key driving factors. This study proposes a mixed-methods framework integrating grounded theory, covariance-based structural equation modeling (CB-SEM), and XGBoost-SHAP. First, grounded theory was applied to in-depth interview data to develop a five-dimensional quality risk framework covering process design and transformation, data flow and collaboration, production execution and process control, quality inspection and data traceability, and personnel capability matching. Second, based on 420 valid questionnaire responses, CB-SEM was used to validate the chain transmission path of risks along “process design–data collaboration–production execution–inspection and traceability” and to reveal the pervasive effect of personnel capability matching risk, with indirect effects accounting for 43.5%. Finally, the XGBoost-SHAP framework was introduced to capture nonlinear and item-level effects, identifying risk items with high predictive contributions, such as the absence of pre-job certification, underlying control failures, and distorted simulation boundary conditions. By integrating qualitative construction, linear validation, and nonlinear attribution, this study provides cross-method evidence for identifying the driving factors and transmission mechanisms of quality risks in equipment digital manufacturing processes. The findings deepen the understanding of how quality risks are formed and transmitted across equipment digital manufacturing processes and provide decision support for agile and targeted digital quality control in equipment manufacturing enterprises.