The impact of digital twin innovation on firm operational performance: a knowledge-based perspective

Abstract

Digital twin (DT) innovation is increasingly integrated into firms' daily operations, yet it is understudied in existing literature. This study adopts a knowledge-based view (KBV) to conceptualize DT innovation as the development of a novel repository of DT-specific knowledge and empirically examines its impact on operational efficiency (OE). Additionally, the study investigates how financial slack and industry dynamism (ID) moderate this relationship, offering a comprehensive perspective on the contextual factors influencing the value of DT innovation. Using a sample of 4,629 listed firms in China, we identify 278 treatment firms based on their first DT patent applications, which serve as event indicators. We employ staggered difference-in-differences (DiD) estimations, further validated by stacked DiD analysis on matched sample, alternative measurements and placebo test. DT innovation raises OE by 4.9%, with the effect being more pronounced among firms with greater financial slack or those operating in highly dynamic industries. Post-hoc analyses further suggest that the benefits of DT innovation are especially significant for smaller firms or those with higher R&D intensity. As an early study investigating how DT innovation impacts firm OE, this research applies KBV in the context of disruptive technologies, and offers guidance for practitioners on mobilizing financial slack and adapting operational strategies to ID in order to better leverage DT innovation for efficiency gains.