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Cited by 953

The DOI matches its OpenAlex record; 15 of the 15 references it cites were found in a registry or an index.

11 Evidence in Management and Organizational Science: Assembling the Field’s Full Weight of Scientific Knowledge Through Syntheses

Denise M. Rousseau, Joshua Manning, David Denyer·2008·Academy of Management Annals
DOI 10.1080/19416520802211651
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Abstract

This chapter advocates the good scientific practice of systematic research syntheses in Management and Organizational Science (MOS). A research synthesis is the systematic accumulation, analysis and reflective interpretation of the full body of relevant empirical evidence related to a question. It is the critical first step in effective use of scientific evidence. Synthesis is not a conventional literature review. Literature reviews are often position papers, cherry‐picking studies to advocate a point of view. Instead, syntheses systematically identify where research findings are clear (and where they aren’t), a key first step to establishing the conclusions science supports. Syntheses are also important for identifying contested findings and productive lines for future research. Uses of MOS evidence, that is, the motives for undertaking a research synthesis include scientific discovery and explanation, improved management practice guidelines, and formulating public policy. We identify six criteria for establishing the evidentiary value of a body of primary studies in MOS. We then pinpoint the stumbling blocks currently keeping the field from making effective use of its ever‐expanding base of empirical studies. Finally, this chapter outlines (a) an approach to research synthesis suitable to the domain of MOS; and (b) supporting practices to make synthesis a collective MOS project.

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Metadata

Authors
Rousseau, D. M., Manning, J., & Denyer, D.
Venue
Academy of Management Annals
Publisher
Routledge
Published
2008-01-01
Volume
2
Issue
1
Pages
475-515
ISSN
1941-6067 (ISSN-L)
DOI
10.1080/19416520802211651
OpenAlex
W4231299816
Cited by
953
References
15
FWCI
17.61
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.