Capitalizing Innovation Capabilities for Smart Value Chain Management in Collaborative Innovation Networks

Muhammad Faraz Mubarak (), Monika Petraite, Mubarra Shabbir, Richard Evans and Anna Pilkova
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Muhammad Faraz Mubarak: Dalhousie University
Monika Petraite: Kaunas University of Technology
Mubarra Shabbir: Kaunas University of Technology
Richard Evans: Dalhousie University
Anna Pilkova: Comenius University Bratislava

Chapter Chapter 4 in Smart Supply Chain Management, 2025, pp 53-71 from Springer

Abstract: Abstract Technology upgrading, which refers to the strengthening of a firm’s technological capabilities, is crucial for maintaining competitiveness by advancing entire value chains through the adoption of smart technologies. The process of upgrading value chains and smartization of value chains including supply chain networks begins with technological learning (TL), where firms acquire, assimilate, and apply knowledge of new and smart technologies to their current operations. By embracing TL, firms can adapt and thrive, thereby creating a competitive edge. This can be achieved through participation in collaborative innovation networks, such as open innovation communities, supply chain networks, and global value chains (GVCs), which provide opportunities to learn from and share experiences with others. In this context, innovation capabilities play a critical role in enhancing TL. However, existing literature on innovation capabilities primarily focuses on the technical dimensions and capabilities of firms’ product development and manufacturing. In contrast, this study adopts a relational view of innovation capabilities, emphasizing the importance of knowledge-based interactions within collaborative innovation networks composed of diverse partners to digitally transform these networks. Specifically, trust, social capital, innovation culture, and networking capabilities are identified as key factors that facilitate effective interactions for technological learning and (their) upgrading. This study employs a twofold research method: first, innovation capabilities are prioritized using the analytical hierarchy process (AHP), and second, interpretive structural modeling (ISM) is used, based on multi-criteria decision-making (MCDM) principles, to identify the interactions and interdependencies among these capabilities. The research further explores how these capabilities directly and indirectly influence technology upgrading through technological learning by engaging 12 experts from policy, research, and academia to rank and investigate the interactions of these capabilities and their influence on upgrading. From a practical perspective, this study offers a foundational model that firms can use to develop strategies for enhancing innovation capabilities, with a focus on the relational view, while also technologically advancing value chain networks. Theoretically, it contributes to the current understanding and discussions on the role and interactions of innovation capabilities in facilitating technology learning and upgrading.

Keywords: Innovation capabilities; Technological learning; Digital transformation; Collaborative innovation networks; Global value chains (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-1333-5_4

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DOI: 10.1007/978-981-96-1333-5_4

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