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Since the late 1980s, there has been no explicit regional policy in Canada. Indirectly, though, equalization payments, industrial policies, as well as regional agencies encouraging the adoption of federal industrial and innovation policies, impact regional economies. In 2017, the federal government appeared to alter its approach: the Supercluster initiative was announced, drawing upon the idea that localized networks of interrelated firms can generate innovation and local development. In this paper, we discuss the mechanisms through which spatially focused industrial innovation policy can lead to regional development. We then focus on Canada's Ocean Supercluster initiative. The question we address is as follows: to what extent can this initiative (and, more widely, Canada's Supercluster policy) be understood as a regional development strategy driven by a coherent rationale for regional intervention? Apart from the fact that each Supercluster focuses on a pre‐existing core of firms located within a region, there is little evidence that the Supercluster initiative has regional development objectives or impacts. Key messages: In 2018, Canada's federal government implemented the Supercluster policy. Five regionally focussed clusters were selected for support for business‐led innovation initiatives.Superclusters were presented as a means both to develop regional economies and to enhance Canada's global innovation impact, in a manner similar to Europe's smart specialization.Despite the initial regional dimension, Superclusters are geared towards national, not regional, objectives and do not focus upon regional development processes. [ABSTRACT FROM AUTHOR]

Organizations are using crowdsourcing to capture innovation knowledge from the crowd in the form of ideas and then using the crowd to evaluate those ideas using votes. In this paper, we investigate a crowdsourcing setting in which Canada solicited information from its citizens to develop a digital transformation strategy. Canada used a two‐phase approach. Phase 1 was used to determine which ideas had the largest number of crowd votes, whereas in Phase 2, the crowd voted on the 30 leading vote‐getting ideas to determine the three winning ideas. This research investigates the ability to use information from ideas to estimate the number of votes that the ideas generate. This approach could be used to estimate the number of ideas, before making information available to the crowd. The unstructured text information in the idea is structured by using target concept dictionaries, which are used to estimate the extent to which the dictionary words appear in the ideas (e.g., "globalism") and are related to the number of votes. Using this approach, roughly 1% of the total words are used to explain roughly 60% of the variance in the votes. Further, we also find that the variables associated with Phase 1 votes are not the same variables associated with Phase 2 votes; that is, the decision‐making variables changed. Finally, we find that votes are statistically significantly related to the content in the idea titles and the idea statements. [ABSTRACT FROM AUTHOR]

Collaborations between and within sectors are common and crucial to the creation and transfer of knowledge. It is often unclear who is involved in the collaboration, and with whom and why they are collaborating. I studied reasons for collaboration and how capital and institutions affect collaboration through a mixed methods analysis of infection and immunity research and development collaborations in Vancouver, Canada between individuals affiliated with universities, firms, and health-care organizations. I found that both capital and institutions were important in collaboration decisions. Collaboration worked as a balancing act between capital and institutions. Potential collaborators needed to offer different capital to the collaboration while supporting the dominant institutions of potential collaborators. Participants' organizational and sectoral affiliations influenced available capital and dominant institutions. These findings help policy makers understand collaboration dynamics between sectors and how translation can occur between universities, firms, and health-care organizations. [ABSTRACT FROM AUTHOR]