The Effects of Servitization, Digitalization, and Digital Learning Orientation on Sustainable Performance: A Hybrid PLS-SEM-ANN Model - Xiaoxia Wu; Yunkai Tang; Xin Fang; Yueling Xu; Sijia Qiao (IEEE Access)
Sustainability is a global concern. Industry 4.0 is driving the manufacturing sector toward sustainability within the Triple Bottom Line (TBL) framework, emphasizing economic, environmental, and social performance. However, limited empirical research has examined how servitization, digitalization, and digital learning orientation (DLO) interact to influence all three dimensions of sustainable performance. This study addresses this gap by proposing a tri-theoretical framework based on TBL, Resource-based View (RBV), and Dynamic Capability View (DCV). A hybrid partial least squares structural equation modeling-artificial neural network (PLS-SEM-ANN) model was developed to examine the direct effects of servitization and digitalization, the mediating effect of digitalization, and the moderating effect of DLO. An empirical analysis of 519 Chinese manufacturing employees revealed that servitization and digitalization positively impact sustainable performance, with digitalization acting as a mediating factor. Servitization also positively impacts digitalization. However, DLO negatively moderates the relationship between servitization and sustainable performance. A case study of Amazon.com, Inc. validates our quantitative findings by demonstrating the impact of servitization, digitalization, and DLO strategies on sustainable performance in real-world applications. These findings reveal theoretical and practical implications for effectively leveraging servitization, digitalization, and DLO to achieve practical sustainability.