Critical mass and the dependency of research quality on group size

Kenna, Ralph; Berche, Bertrand

doi:10.1007/s11192-010-0282-9

Cited by 56 publications

(73 citation statements)

References 8 publications

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“…The majority of studies do not find a positive relation between research productivity and group size (Hemlin & Ollson 2013). Data rather indicate that research groups tend to be most productive if a certain threshold in size is not exceeded (Kenna & Berche 2011), a finding that is also supported by general theoretical considerations (Kao & Couzin 2014). The problem seems to be further aggravated when the disciplinary diversity is increased (Cummings et al 2013), which is a characteristic of Big Neuroscience due to the involvement of information technology (for a further discussion of this point see Section 4).…”

Section: Big Numbermentioning

confidence: 90%

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Ethical Challenges of Simulation-Driven Big Neuroscience

Christen

Biller‐Andorno

Bringedal³

et al. 2016

AJOB Neuroscience

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Research in neuroscience traditionally relies on rather small groups that deal with different questions on all levels of neuronal organization. Recent funding initiatives-notably the European "Human Brain Project" (HBP)-aim to promote Big Neuroscience for integrating research and unifying knowledge. This approach is characterized by two aspects: first, by many interacting researchers from various disciplines that deal with heterogeneous data and are accountable to a large public funding source; and second, by a decisive role of information and communication technology (ICT) as an instrument not only to perform but also to structure and guide scientific activities, for example, through simulations in the case of the HBP. We argue that Big Neuroscience comes along with specific ethical challenges. By examining the justification of Big Neuroscience and the role and effects of ICT on social interaction of researchers and knowledge production, we provide suggestions to address these challenges.

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Section: Big Numbermentioning

confidence: 90%

“…However, authors have argued that the usual way to measure research productivity -in terms of publications and citations (Kenna & Berche 2011;Fortin & Currie 2013) -is not adequate to measure…”

mentioning

confidence: 99%

Ethical Challenges of Simulation-Driven Big Neuroscience

Christen

Biller‐Andorno

Bringedal³

et al. 2016

AJOB Neuroscience

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“…Outro fator que pode influenciar a produtividade acadêmica é o tamanho do grupo de pesquisa, uma vez que o aumento do número de pesquisadores pode provocar uma elevação da sua produtividade, dadas as maiores possibilidades de divisão do trabalho e de aproveitamento das complementaridades entre os membros do grupo de pesquisa (Abramo et al 2012). Porém, a existência de grupos de pesquisa muito grandes pode gerar dificuldades de coordenação entre os pesquisadores (Von Tunzelmann et al 2003), o que sugere a existên-cia de um tamanho "ótimo" dos grupos de pesquisa (Kenna & Berche 2011). Já no que diz respeito à qualificação, grupos de pesquisa que possuem pesquisadores mais graduados entre os seus membros possuem produtividade acadêmica mais elevada (Abramo et al 2011).…”

Section: A Nova Economia Da Ciência E a Interação Da Universidade Comunclassified

An analysis of the effects of the interaction of university with firms on academic productivity

et al. 2017

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ResumoO objetivo deste trabalho é analisar o efeito das colaborações da universidade com as empresas sobre a produtividade acadêmica dos grupos de pesquisa no Brasil. Para isso, foi utilizada uma técnica de pareamento por propensity score para formação de grupos de controle, o que permitiu comparar, por meio da estimação de dois modelos empíricos, a produtividade acadêmica dos grupos de pesquisa que colaboram com empresas com a dos grupos que não interagem. Os principais resultados mostram que a interação com as firmas exerce efeitos positivos sobre a produtividade acadêmica dos grupos de pesquisa e esse efeito é amplificado no caso dos grupos de pesquisa que interagem de modo contínuo com empresas, em comparação com os grupos de pesquisa que interagem pontualmente. Palavras-chave:Interação universidade-empresa; produtividade acadê-mica; conhecimento e inovação. AbstractThe aim of this paper is to analyse the effect of the university collaboration with firms on the academic productivity of research groups in Brazil. To do that, the propensity score matching technique was used to build a control group in order to compare, through the estimation of two empirical models, the academic productivity of research groups that collaborate with firms to those that do not interact. Main results show that interactions with firms has positive effects on academic productivity of research groups and this effect is enlarged for research groups that interact continuously with firms, compared to those that interact sporadically.

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“…Research groups can be viewed as complex systems that depend on a variety of forms of interaction and knowledge exchange (Kenna and Berche 2011). Generally, applied fields tend to benefit more from larger groups than theoretical fields.…”

Section: Critical Massmentioning

confidence: 99%

The size of research funding: Trends and implications

Bloch

Sørensen

2014

Science and Public Policy

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This paper examines the role of grant size in research funding. There is an increasing focus in a number of countries on larger grant forms, such as centers of excellence, and in some cases also increases in the size of individual project grants. Among the rationales for this are economies of scale in research and redistribution of resources towards top researchers in order to increases scientific productivity and pathbreaking research. However, there may potentially also be negative impacts of increasing funding size, and there is limited empirical evidence on the actual consequences of increases in size. In this paper we critically examine the rationales behind increases in funding size and the empirical evidence on the impacts of size in research funding. Our goal here is to present a more coherent view of the potential impacts of these initiatives, both positive and negative, that can help inform funding design.

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Critical mass and the dependency of research quality on group size

Cited by 56 publications

References 8 publications

Ethical Challenges of Simulation-Driven Big Neuroscience

Ethical Challenges of Simulation-Driven Big Neuroscience

An analysis of the effects of the interaction of university with firms on academic productivity

The size of research funding: Trends and implications

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