Research on the Selection of Multi-Agent Interaction Modes in Complex Product R&D Networks Under Disruption Events

The negative impact of disruption events (i.e., departure of key personnel, geopolitical conflicts) on normalized economic operations is self-evident. How complex product R&D networks maintain resilience under disruptions is an unavoidable management issue. Existing research has primarily focused on the effects of inter-organizational interactions under disruption events, while paying little attention to the diversity of multi-agent interactions in complex product R&D network and the deeper logic embedded within them. Combining the attention-based view and sequential attention logic, we take the resilience of complex product R&D networks as the dependent variable to study the selection effects of multi-agent interaction modes in complex product R&D networks under disruption events. A hierarchical linear regression was conducted on 427 questionnaire samples from the high-end equipment manufacturing industry. Empirical results show that complex product R&D networks are prone to two multi-agent interaction modes under disruption events: the focal firm-dominated mode and the multi-agent co-creation mode. Compared with the latter, focal firms show a stronger preference for the former mode in disruptions. Both multi-agent interaction modes contribute to enhancing network resilience. Further research shows that under first-order disruption events (i.e., departure of key personnel), both focal firm-dominated mode and the multi-agent co-creation mode serve as effective resilience governance strategies for complex product R&D networks, with the multi-agent co-creation mode being better than the focal firm-dominated mode. Under second-order disruption events (i.e., geopolitical conflicts), only the multi-agent co-creation mode emerges as an important mechanism for resilience governance of complex product R&D networks. The findings provide a theoretical foundation for complex product R&D networks to select the optimal multi-agent interaction mode to enhance resilience and effectively respond to disruption events.