Complexity sciences: towards an alternative approach to understanding the use of academic research

Lemay, Margaret A.; Sá, Creso M.

doi:10.1332/174426412x660133

Cited by 13 publications

(10 citation statements)

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“…Knowledge mobilisation researchers 20,26,27 have argued that the 'complex systems' theories that are beginning to permeate thinking on health care delivery need also to be applied in thinking about knowledge use. There is growing emphasis on the need to attend to issues around power and conflict over what constitutes 'knowledge' in a given time and context 16,24,[28][29][30] rather than treating it as an objective and unproblematic entity.…”

Section: Framing the Problemmentioning

confidence: 99%

Mobilising knowledge to improve UK health care: learning from other countries and other sectors – a multimethod mapping study

Davies

Powell

Nutley

2015

Health Services and Delivery Research

114

176

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BackgroundThe past two decades have seen rich conceptual development and a wide variety of practical initiatives around research use or ‘knowledge mobilisation’, but so far there has been little systematic effort to map, conceptualise and learn from these initiatives, or to investigate the degree to which they are underpinned by contemporary thinking as set out in the literature. This gap is particularly apparent when looking at knowledge mobilisation at the ‘macro’ level, that is the strategies and activities of major research funders, major research producers and key research ‘intermediaries’.Aims and objectivesThe study had three key objectives with associated research questions: to map the knowledge mobilisation landscape in health care (in the UK and internationally) and in social care and education within the UK; to understand the models, theories and frameworks that underpin the approaches to knowledge mobilisation; and to learn from the success or otherwise of the strategies and approaches in use.MethodsThe study was multimethod and multiphased, with considerable interactivity between the different strands. Data were collected through a review of 71 published reviews on knowledge mobilisation; website review of the knowledge mobilisation activities of 186 agencies; in-depth interviews (n = 52) with key individuals in agencies; a web survey (response rate 57%;n = 106); and two stakeholder workshops (at months 6 and 16).FindingsWe identified a wide range of models, theories and frameworks used to describe knowledge mobilisation and created a conceptual map that highlights six domains of thinking and debate in the literature. The interview and survey data showed three broad, overlapping roles undertaken by agencies: developing and sharing research-based products; emphasising brokering; and focusing on implementation. The knowledge mobilisation approaches in use had been shaped by many factors but there was only limited use of the models, theories and frameworks from the literature. Participants saw formal evaluation of knowledge mobilisation activities as important but highly challenging. Rich formative experience was described but formal evaluation was relatively rare. Few agencies involved service users or members of the public in knowledge mobilisation activities. Working inductively from the study data we derived eight key archetypes or ‘bundles of knowledge mobilisation activities’ that could be used by agencies to explore their knowledge mobilisation activities, future strategies and stakeholder perspectives.ConclusionsKnowledge mobilisation could be enhanced by providing support to enable cross-sector and interagency learning, reflection on the conceptual basis of approaches and increased evaluation of knowledge mobilisation activities. Further research is needed to evaluate approaches to assessing research use and impact, on systems approaches to knowledge mobilisation, on sustaining and scaling-up approaches, and on applying a wider range of literatures to knowledge mobilisation. Further research would also be useful on the knowledge mobilisation archetypes and how they can work in complementary ways.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

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Section: Framing the Problemmentioning

confidence: 99%

Mobilising knowledge to improve UK health care: learning from other countries and other sectors – a multimethod mapping study

Davies

Powell

Nutley

2015

Health Services and Delivery Research

114

176

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“…Hence, this understanding helps challenge simplified linear models of innovation as a series of sequential steps undertaken by different actors (see discussion by Balconi et al 2010). Instead, the production and use of research are linked by an ongoing, reciprocal process of myriad network interactions, other forms of indirect or spontaneous connections, and information feedbacks within a complex system, and bring about the co-evolution of its interdependent components (Lemay and Sá 2012).…”

Section: Universities and Territorial Adaptabilitymentioning

confidence: 99%

“…McGlade and Garnsey 2006;Martin and Sunley 2007;Lemay and Sá 2012). AsMartin and Sunley (2007, p.591-592) argue: [T]he main difference between complexity economics and neo-Darwinian views of economic evolution appears to rest on the relative importance of system selftransformation relative to selection.…”

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confidence: 99%

Universities, Public Research, and Evolutionary Economic Geography

Vallance

2016

Economic Geography

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Evolutionary Economic Geography (EEG) has, thus far, neglected the contribution of universities to innovation processes in its emerging theoretical explanations of territorial economic change. This paper begins to address this conceptual gap by outlining a perspective on the ways in which universities, as organizations with institutional features and functions that are distinctive to those of firms, can enhance the adaptive capacity of national or regional economies. The argument developed is based on a complexity theory view of system selftransformation and supports greater attention to this framework in a pluralistic EEG.

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“…It is a non-linear process with feedback between the different stages of development. Innovation is an emergent property of a complex adaptive social system [54][55][56][57][58][59][60].…”

Section: Innovation Systems and Scaling Propertiesmentioning

confidence: 99%

What is a Complex Innovation System?

Katz

2015

SSRN Journal

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Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kx α , where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too.

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Complexity sciences: towards an alternative approach to understanding the use of academic research

Cited by 13 publications

References 0 publications

Mobilising knowledge to improve UK health care: learning from other countries and other sectors – a multimethod mapping study

Mobilising knowledge to improve UK health care: learning from other countries and other sectors – a multimethod mapping study

Universities, Public Research, and Evolutionary Economic Geography

What is a Complex Innovation System?

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