ORGANIZATIONAL DRIVERS OF INNOVATION-DRIVEN VALUE CO-CREATION IN MANUFACTURING LOGISTICS NETWORKS UNDER TECHNOLOGICAL AND INFORMATION UNCERTAINTY

Abstract

Contemporary globalization and the emergence of Industry 4.0 have shifted competition from individual firms to integrated logistics networks, foregrounding the co-creation of innovative value as a key condition for supply-chain performance. Escalating technological and informational uncertainty undermines the effectiveness of classical coordination mechanisms and calls for new approaches to organizing cooperative networks. This article aims to advance the theoretical foundations and develop methodological recommendations for organizing the co-creation of innovative value among participants in logistics networks of manufacturing collaboration under technological and informational uncertainty. We employ a systems perspective, theoretical synthesis, Organizational Information Processing Theory (OIPT), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and organizational process modelling. The study is grounded in the theory of dynamic capabilities, service-dominant logic, principles of innovation management, and network governance. We propose a taxonomy of organizational determinants of innovative value co-creation comprising four groups: architectural–structural (modularity and interoperability), behavioral–relational (trust and partnership), resource- and capability-based (flexibility of network contracts), and institutional (alignment of norms and rules). An integrative framework has been developed to trace how organizational potential is transformed into co-created innovative value. We introduce a composite evaluation of co-creation effectiveness based on contrasting leading and lagging indicators. A causal model is constructed that links organizational determinants to the mechanisms of innovative value creation. We further elaborate a methodological approach for assessing each network participant’s contribution through a weighted aggregation of effects. The results can inform organizational modelling of manufacturing collaboration networks, optimize role allocation among participants, and enhance the effectiveness of innovation activities under technological and informational uncertainty.