2018 ◽  
Vol 48 ◽  
pp. 01045
Author(s):  
Marta Majorek ◽  
Marta du Vall

Number of factors may be indicated that can negatively affect the willingness of third sector actors and citizens to get involved with research and innovation policy. It is worth referring to these factors as barriers to societal engagement. Six key barriers can be identified: lack of knowledge and skills, lack of relevance, lack of impact, lack of trust and critical views of others, lack of time and finances, and lack of legitimacy. The main purpose of this article is to present some policies and practice options that can help to overcome these barriers. The main focus will be on solving the lack of knowledge and skills of social actors. In this context, the fact that citizens and third sector actors may perceive an engagement process as not relevant to their own interests, concerns, and goals may be indicated as the main cause for their non-involvement. Citizens and third sector actors may refrain from engagement when they fear they lack the necessary knowledge and skills to engage in research or in research and innovation policy. They may also be reluctant to participate when they do not have the basic understanding of science and scientific working methods. The article will propose an overview of policies and activities that can effectively overcome the indicated barriers to engagement.


Author(s):  
Pietro Moncada-Paternò-Castello ◽  
Sara Amoroso ◽  
Michele Cincera

Abstract Research and Development (R&D) indicators are used to facilitate international comparisons and as targets for research and innovation policy. An example of such an indicator is R&D intensity. The decomposition of the aggregate corporate R&D intensity is able to explain the differences in R&D intensity between countries by determining whether is the result of firms’ underinvestment in R&D or of the differences across sectors. Despite its importance, the literature of corporate R&D intensity decomposition has been developed only recently. This article reviews for the first time the different methodological frameworks of corporate R&D intensity decomposition and how they are used in practice, shedding light on why sometimes empirical results seem to be contradictory. It inspects how the use of different data sources and analytical methods affect R&D intensity decomposition results, and what the analytical and policy implications are. The article also provides methodological and analytical guidance to analysts and policymakers.


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