Cruise ship manufacturing is a high-tech, complex industry where development depends on coordination across stages and organizations. We advance the coordination literature by treating the supply chain, industry chain, and value chain as a complex system, and by linking cross-chain coordination to high-quality development in a way that is comparable to theoretical debates on capability building and productivity-oriented development. Empirically, we collect city-level panel data for ten Chinese cruise port cities from 2008 to 2023 and combine a coupling–coordination framework with a panel data qualitative comparative analysis (PD-QCA) to capture both coordination dynamics and configurational causality. Our results show substantial heterogeneity in coordination trajectories, which can be grouped into decline–recovery, high-level stability, and persistent decline/high-variability patterns. We also show that high coupling does not guarantee high-quality outcomes, which are jointly shaped by industrial foundations, high-end value creation, and innovation capacity. Moreover, we identify two main pathways: an anchoring pathway that depends on output capacity and resource inputs, and an optimizing pathway that mainly relies on investment intensity, demand-side output, and value efficiency, with cross-chain coordination acting as an enabling condition that helps improve cross-chain matching.

The success of a cluster depends on the interplay between intra- and inter-regional collaboration, while empirical research on the mechanisms through which intra- and inter-regional collaborations trigger cluster growth is rarely focused. Based on the local buzz–global pipelines framework and incorporating an evolutionary perspective, we explore how intra- and inter-regional innovation networks can be integrated to drive the growth of emerging industries. We draw on Zhangjiang, China’s most advanced AI industry cluster, using social network analysis and qualitative methods, combining patent data with semi-structured interviews. The results indicate that with the return of multinational corporations and the limitation of Western technology, universities have become the primary source of AI innovation in Zhangjiang. The government has played a pivotal role in propelling the accelerated growth of China’s AI industry, particularly through the backing of pioneering AI private enterprises and the calibration of the potential inefficiencies associated with a state-led model with the dynamism of market forces. The ‘Buzz-and-pipeline’ dynamics in the AI innovation network are shaped by four processes: local networking, market demand, resource integration, and policy synergy. This underscores the intricate interconnections between the national and local scales in AI innovation networks.