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Cited by 267

The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.

Methodological Wishes for the Next Decade and How to Make Wishes Come True

Open access
Aguinis, Herman, Edwards, Jeffrey R.·2014·Journal of Management Studies

Abstract

We discuss seven methodological improvements that would stimulate important advancements in management research. We refer to these improvements as ‘wishes’ that we hope will materialize within the next decade. To promote the implementation of these improvements, we offer concrete and actionable recommendations that researchers can apply when designing and conducting empirical research and that journal editors and reviewers can consider when evaluating manuscripts for publication. These improvements address: (1) accelerating theoretical progress; (2) improving the construct validity of measures; (3) strengthening causal inferences; (4) incorporating multilevel design, measurement, and analysis; (5) balancing trade‐offs between internal and external validity, (6) understanding the nature and impact of outliers; and (7) offering a realistic and useful description of a study's limitations.

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Metadata

Authors
Aguinis, H., & Edwards, J. R.
Venue
Journal of Management Studies
Publisher
Wiley
Published
2013-10-10
Volume
51
Issue
1
Pages
143-174
ISSN
  • 0022-2380 (ISSN-L)
  • 1467-6486
OpenAlex
W1502064375
Cited by
267
References
180
FWCI
21.31
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Methodological Wishes for the Next Decade and How to Make Wishes Come True. https://app.cochat.ai/papers/10.1111/joms.12058
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Aguinis, H., & Edwards, J. R. (2014). Methodological Wishes for the Next Decade and How to Make Wishes Come True. Journal of Management Studies. https://doi.org/10.1111/joms.12058

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.