How European public sector agencies innovate: The use of bottom-up, policy-dependent and knowledge-scanning innovation methods

Anthony Arundel, Luca Casali and Hugo Hollanders

Research Policy, 2015, vol. 44, issue 7, 1271-1282

Abstract: Factor and cluster analysis are used to identify different methods that public sector agencies in Europe use to innovate, based on data from a 2010 survey of 3273 agencies. The analyses identify three types of innovative agencies: bottom-up, knowledge-scanning, and policy-dependent. The distribution of bottom-up agencies across European countries is positively correlated with average per capita incomes while the distribution of knowledge-scanning agencies is negatively correlated with income. In contrast, there is no consistent pattern by country in the distribution of policy-dependent agencies. Regression results that control for agency characteristics find that innovation methods are significantly correlated with the beneficial outcomes of innovation, with bottom-up and knowledge-scanning agencies out-performing policy-dependent agencies.

Keywords: Public sector innovation; Taxonomy of innovation; Innovation outcomes; Innovation survey (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0048733315000670
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:44:y:2015:i:7:p:1271-1282

DOI: 10.1016/j.respol.2015.04.007

Access Statistics for this article

Research Policy is currently edited by M. Bell, B. Martin, W.E. Steinmueller, A. Arora, M. Callon, M. Kenney, S. Kuhlmann, Keun Lee and F. Murray

More articles in Research Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().