Purpose: This study explores the combined effects of structural and relational embeddedness within alliance networks on firm innovation. By focusing on the interplay between network structures and relationships, this study provides a nonlinear framework to unravel the complex dynamics between alliance networks and firm innovation performance within the manufacturing industry. Design/methodology/approach: Using social network analysis, this study examines the topological structure of firms’ alliance networks. An exploratory approach involving K-Means clustering and decision tree methods is employed to identify heterogeneous network types within the alliance networks. The analysis further explores the nonlinear relationships between network characteristics, including closeness centrality, betweenness centrality, clustering coefficient, and relational attributes, including collaboration intensity and breadth, and their combined influence on firm innovation. Findings: The study identified four distinct heterogeneous network types: dyadic, star, ringlike, and complex networks. Each type reveals unique network characteristics and their impact on innovation performance. Key decision rules were extracted, showing that strong relational embeddedness can hinder innovation in dyadic networks, while a greater distance from the central firm correlates with higher innovation performance in star alliance networks. For ringlike alliance networks, moderate cooperation intensity is beneficial for innovation when the clustering coefficient is not high. In complex alliance networks, the combined effects of cooperation intensity, breadth, and clustering coefficient significantly influence innovation. Research limitations: The research presented in this study, while offering valuable insights into the relationship between alliance networks and firm innovation within the manufacturing sector, is subject to several limitations. A focus on the manufacturing industry may restrict the generalizability of our findings to other sectors, where the dynamics of innovation and collaboration might differ significantly. Additionally, our reliance on patent data, while providing a quantifiable measure of innovation, may overlook other forms of innovation that are equally critical in different contexts, such as service innovations or business model transformations. Practical implications: This research offers significant insights into how firms can leverage both network structure and relational aspects to enhance innovation outcomes. By revealing the nonlinear and complex interactions between network embeddedness dimensions, this study makes a valuable contribution to both theory and practice. This highlights that strategic management of both structural and relational embeddedness can foster superior innovation performance, offering firms a competitive advantage by optimizing their alliance network configurations. Originality/value: This study’s originality lies in its examination of the combined effects of structural and relational network embeddings on innovation performance. By identifying distinct network types and their impact on innovation, this study advances the theoretical understanding of how network characteristics interact to shape firm innovation. It contributes to the literature by offering a novel, multidimensional framework that integrates social network theory and resource-based view, providing new insights for firms to leverage their network positions and relationships for competitive advantage.

Purpose: This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks. Design/methodology/approach: This study employs a mixed-method approach, combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance. Using a patent dataset of Chinese industrial enterprises, the research constructs the collaboration networks and analyzes their structural positions, particularly focusing on their role as intermediaries, characterized by betweenness centrality. Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes. Findings: The study reveals that firms in intermediary positions enhance collaborative innovation performance, but this effect is nuanced. A key finding is that network clustering negatively moderates the intermediary-innovation relationship. Highly clustered networks, while fostering local collaboration, may limit the innovation potential of intermediaries. On the other hand, relationship strength, measured by collaboration intensity and trust among firms, positively moderates the intermediary-innovation link. Research limitations: This study has several limitations that present opportunities for further research. The reliance on quantitative social network analysis may overlook the complexity of intermediaries’ roles, and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors. Additionally, cross-country comparisons are needed to assess the consistency of these dynamics in different contexts. Practical implications: The study offers practical insights for firms and policymakers. Organizations should strategically position themselves as network intermediaries to access diverse information and resources, thereby improving innovation performance. Building strong trust helps using network intermediary advantages. For firms in highly clustered networks, it is important to seek external partners to avoid limiting their exposure to new ideas and technologies. This research emphasizes the need to balance network diversity with relationship strength for sustained innovation. Originality/value: This research contributes to the literature by offering new insights into the role of network intermediaries, presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

Understanding regional disparities in Chinese modernization is essential for achieving coordinated and sustainable development. This study develops a multi-dimensional evaluation framework, integrating grey relational analysis, entropy weighting, and TOPSIS to assess provincial modernization across China from 2018 to 2023. The framework operationalizes Chinese-style modernization through five dimensions: population quality, economic strength, social development, ecological sustainability, innovation and governance, capturing both material and institutional aspects of development. Using K-Means clustering, kernel density estimation, and convergence analysis, the study examines spatial and temporal patterns of modernization. Results reveal pronounced regional heterogeneity: eastern provinces lead in overall modernization but display internal volatility, central provinces exhibit gradual convergence, and western provinces face widening disparities. Intra-regional analysis highlights uneven development even within geographic clusters, reflecting differential access to resources, governance capacity, and innovation infrastructure. These findings are interpreted through modernization theory, linking observed patterns to governance models, regional development trajectories, and policy coordination. The proposed framework offers a rigorous, data-driven tool for monitoring modernization progress, diagnosing regional bottlenecks, and informing targeted policy interventions. This study demonstrates the methodological value of integrating grey system theory with multi-criteria decision-making and clustering analysis, providing both theoretical insights and practical guidance for advancing balanced and sustainable Chinese-style modernization.

Alliance network embeddedness provides firms with access to external knowledge and resources, yet its innovation implications vary across firms and network contexts. This study examines how network structure and combinations of embedding characteristics are associated with corporate innovation performance. Based on 335 firm-level observations from Chinese listed biopharmaceutical manufacturing firms, the study first identifies heterogeneous alliance network environments through community detection and K-Means clustering. Four network types are identified: dyadic, ringlike, star, and complex alliances. Classification and regression trees (CART) are then employed to extract interpretable, threshold-based decision rules linking network embedding characteristics to high and non-high innovation performance. The results show that no single network characteristic is consistently associated with innovation performance across alliance types. In dyadic alliances, moderate cooperation intensity is associated with high innovation performance, whereas ringlike alliances exhibit conditional associations involving cooperation intensity and partner centrality. Star alliances are characterized by configurations involving cooperation breadth and network position, while complex alliances exhibit more multidimensional combinations of structural and relational conditions. The findings indicate that the innovation relevance of alliance network embeddedness is network-type-specific and configuration-dependent. As a complementary robustness analysis, fuzzy-set qualitative comparative analysis broadly supports several core configurational patterns identified by CART, while also revealing alternative configurations, particularly in complex alliances. The study demonstrates that understanding alliance network embeddedness requires attention to network context, empirical thresholds, and combinations of network characteristics rather than isolated network attributes.