How does alliance network embedding affect firm innovation? Evidence from the Chinese manufacturing industry
Authors: Zhang Zhiwei , Zhou Wenhao , Li Hailin 
Published: February 2025

Abstract

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.