Innovation in Manufacturing Within the Digital Intelligence Context: Examining Faultlines Through Information Processing

In the context of digital intelligence, innovation is vital for manufacturing enterprises to establish sustainable competitive advantages. As the cornerstone of decision-making, the information-processing capability of top management teams plays an essential role in driving organizational success. Using panel data from A-Share manufacturing listed companies between 2015 and 2023, we conducted programming in the R language employing hierarchical clustering and k-means algorithms for faultline grouping calculations. The empirical analysis portion utilized STATA software, where the Hausman test was implemented to determine the use of a fixed-effects model for computation. The results demonstrate that task-related faultlines, driven by factors such as educational background, tenure, career experience, and years of service, have a positive impact on innovation performance. In contrast, relationship-related faultlines influenced by gender and age exhibit a negative effect. Furthermore, long-term investment decision preferences mediate the relationship between faultlines and innovation performance. Performance expectation gaps amplify the positive influence of task-related faultlines and mitigate the negative effects of relationship-related faultlines. In comparison with the majority subgroup, when the chairperson is part of a minority subgroup, the faultline has a more significant impact on innovation performance. This study presents a novel framework for fostering innovation within the manufacturing industry under the digital intelligence context. By combining R programming with empirical analysis, we thoroughly examine how the characteristics of top management teams’ faultlines influence innovation performance through an information processing perspective. Our findings provide actionable insights for optimizing executive structures and aligning decision-making strategies, thereby advancing organizational effectiveness.