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Macro‐level diffusion of a methodological knowledge innovation: Research synthesis methods, 1972–2011

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  • Laura Sheble

Abstract

Use of research synthesis methods has contributed to changes in research practices. In disciplinary literatures, authors indicate motivations to use the methods include needs to (a) translate research‐based knowledge to inform practice and policy decisions, and (b) integrate relatively large and diverse knowledge bases to increase the generality of results and yield novel insights or explanations. This review presents two histories of the diffusion of research synthesis methods: a narrative history based primarily in the health and social sciences; and a bibliometric overview across science broadly. Engagement with research synthesis was strongly correlated with evidence‐based practice (EBP), and moderately with review prevalence. The social sciences were most diverse in terms of when research synthesis was adopted. Technology, physical sciences, and math appear to be relatively resistant though fields such as physics may be considered to have used similar methods long ago. Additional research is needed to assess the consequences of adoption within fields, including changes in how researchers engage with knowledge resources. This review demonstrates that particularistic histories of science and technology may be fruitfully augmented with informetrics to examine how disciplinary diffusion narratives coincide with patterns across science more broadly, thereby opening up disciplinary knowledge to inform future research.

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  • Laura Sheble, 2017. "Macro‐level diffusion of a methodological knowledge innovation: Research synthesis methods, 1972–2011," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2693-2708, December.
  • Handle: RePEc:bla:jinfst:v:68:y:2017:i:12:p:2693-2708
    DOI: 10.1002/asi.23864
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    1. Borrett, Stuart R. & Sheble, Laura & Moody, James & Anway, Evan C., 2018. "Bibliometric review of ecological network analysis: 2010–2016," Ecological Modelling, Elsevier, vol. 382(C), pages 63-82.

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