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Data Organization and Management Action Group

Purpose
This paper aims to investigate the role of relational governance in innovation platform development, specifically investigating the context of living labs.


Design/methodology/approach
Two longitudinal case studies are presented, derived from auto-ethnographic narratives, qualitative interviews and secondary documents, which cover the critical stages in the development of each living lab.


Findings
Empirical insights demonstrate the relevance of coordination activities based on joint planning and activities to support innovation platform development across different stages. The governance role of research actors as platform activators is also identified.


Practical implications
The paper offers a useful perspective for identifying collective goals between living lab actors and aligning joint activities across different stages of living lab development.


Social implications
The case provides insights into the challenges and opportunities for collaboration between academia, industry and users to support sustainable construction innovation.


Originality/value
A relational governance mode is identified, going beyond top down or bottom up approaches, which contributes a new understanding of how collective goals align within a relational space.

AbstractInternational trade has been increasingly organized in the form of global value chains (GVCs). In this paper, we provide a new method for comparing GVCs across countries and over time. First, we use the World Input–Output Database (WIOD) to construct both the upstream and the downstream global value networks. Second, we introduce a network-based measure of node similarity to compare the GVCs between any pair of countries for each sector and each year available in the WIOD. Our network-based similarity is a better measure for node comparison than the existing ones because it takes into account all the direct and indirect relationships between the country–sector pairs, is applicable to both directed and weighted networks with self-loops, and takes into account externally defined node attributes. As a result, our measure of similarity reveals the most intensive interactions among the GVCs across countries and over time. From 1995 to 2011, the average similarity between sectors and countries have clear increasing trends, which are temporarily interrupted by the recent economic crisis. This measure of the similarity of GVCs provides quantitative answers to important questions about dependency, sustainability, risk, and competition in the global production system.

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Action Group Reports