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Science & Technology Management and Innovation Management

Research on the Impact of Network Embeddedness and Failure  Learning on Value Co-creation in Innovation Ecosystems: An  Empirical Analysis Based on NCA and fsQCA 

Wang Zhanzhao1, 2 , Ma Qing1 , Guan Yujie1 , Wu Yanjuan3

(1.School of Business Administration Research Center for Energy Economics, Henan Polytechnic University, Jiaozuo 454000, China; 2.School of Business Administration, Henan Polytechnic University, Jiaozuo 454000, China; 3.School of Economics and Business, Jeonju University, Jeonju 55069, South Korea)

Abstract: Value co-creation within innovation ecosystems has become a crucial pathway for  enterprises to achieve collaborative innovation and value enhancement. However, the knowledge  resources acquired by enterprises vary across different network embeddedness contexts, and the  choice of failure learning approaches may also impact the efficiency of knowledge resource utili⁃ zation and co-creation outcomes. Therefore, based on knowledge base theory, this paper con⁃ structs a collaborative framework integrating "network embeddedness-failure learning". It com⁃ bines five conditional variables under the network embeddedness dimension—centrality, struc⁃ tural holes, embeddedness scale, relationship breadth, and relationship strength under the net⁃ work embeddedness dimension, with exploratory failure learning and exploitative failure learning  under the failure learning dimension. Taking provinces and cities along the Yangtze River Eco⁃ nomic Belt as the research area, this study employs Necessity-Condition Analysis (NCA) com⁃ bined with Fuzzy Set Qualitative Comparative Analysis (fsQCA) to analyze the influence mecha⁃ nisms of network embeddedness and failure learning on value co-creation within innovation eco⁃ systems. The findings reveal that: ①High-level value co-creation within innovation ecosystems re⁃ sults from the combined effects of network embeddedness and failure learning; no single factor  alone constitutes a necessary condition for the outcome variable. ②Five configuration patterns  emerge for achieving high-level value co-creation in innovation ecosystems: StructuralRelational Synergy—Exploration of Failure Learning Configuration; Structure Dominant—DualFailure Learning Configuration; Relational Dominant—Dual Failure Learning Configuration; Structural Holism*Relational Intensity Synergy—Exploration of Failure Learning Configuration; Centrality*Embeddedness Scale*Relational Breadth Synergy—Exploitation of Failure Learning  Configuration. ③ Four configuration types lead to low-level innovation ecosystem value cocreation, exhibiting an asymmetric relationship with the driving pathways of high-level innova⁃ tion ecosystem value co-creation. The theoretical contributions of this paper mainly include three aspects. First, this study en⁃ riches research on antecedent factors influencing value co-creation within innovation ecosys⁃ tems. By categorizing network embeddedness into structural embeddedness and relational em⁃ beddedness, it identifies five variables: centrality, structural holes, embeddedness scale, rela⁃ tional breadth, and relational strength. Furthermore, it classifies failure learning into exploratory  failure learning and exploitative failure learning, thereby expanding research on the combined ef⁃ fects of multiple antecedent conditions for value co-creation in innovation ecosystems. Second, this study reveals distinct driving pathways for value co-creation within innovation ecosystems, elucidating the complex interplay of multiple conditions underlying this process and offering new  insights for achieving higher levels of value co-creation. Finally, it broadens the application  scope of combined NCA and fsQCA methodologies, providing crucial methodological guidance  for understanding the necessary and sufficient relationships among network embeddedness, failure learning, and value co-creation in innovation ecosystems. 

Key words: network embeddedness; failure learning; innovation ecosystems; value cocreation; NCA; fsQCA; the Yangtze River Economic Belt; knowledge-based theory