Servitization capabilities and network-level performance in manufacturing: evidence from the plant engineering industry
TL;DR
A multi-level framework to examine how classical and digital servitization capabilities, enabled by predictive/analytical artificial intelligence (AI), link to operational performance shows that neither classical nor digital servitization capabilities significantly link to firm-level performance.
Abstract
Abstract In volatile manufacturing environments, particularly in project-based plant engineering, servitization is regarded as a strategic approach to enhance competitiveness and resilience. However, empirical evidence on the relationship between servitization capabilities and firm- and/or network-level performance remains limited. This study develops and tests a multi-level framework to examine how classical and digital servitization capabilities, enabled by predictive/analytical artificial intelligence (AI), link to operational performance. A sequential mixed-methods design combined expert interviews and structural equation modelling based on survey data. Qualitative findings identified digitalization, customer relationship management, and operational excellence as key capability domains. Quantitative results show that neither classical nor digital servitization capabilities significantly link to firm-level performance. Instead, both capabilities strongly link to network-level coordination, collective operational performance, and supply chain resilience. The findings shift the performance focus of servitization from individual firms to manufacturing ecosystems and highlight network orchestration and capability renewal as key drivers of competitive advantage.