The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Addressing Common Method Variance: Guidelines for Survey Research on Information Technology, Operations, and Supply Chain Management
Abstract
Common method variance (CMV) is the amount of spurious correlation between variables that is created by using the same method-often a survey-to measure each variable. CMV may lead to erroneous conclusions about relationships between variables by inflating or deflating findings. We analyzed recent survey research in IEEE Transactions on Engineering Management, Journal of Operations Management, and Production and Operations Management to assess if and how scholars address CMV. We found that two-thirds of the relevant articles published between 2001 and 2009 did not formally address CMV, and many that did address CMV relied on relatively weak remedies. These findings have troubling implications for efforts to build knowledge within information technology, operations and supply chain management research. In an effort to strengthen future research designs, we provide recommendations to help scholars to better address CMV. Given the potentially severe effects of CMV, authors should apply the recommended CMV remedies within their survey-based studies, and reviewers should hold authors accountable when they fail to do so.
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Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
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Barney, J. B. (2004) · Advances in Strategic Management · doi:10.1016/s0742-3322(00)17018-4 · Cited by 38,152
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The Content Analysis Guidebook
Neuendorf, K. A. (2017) · doi:10.4135/9781071802878 · Cited by 9,286 · cited 1×
“agreement, the remaining articles were evaluated primarily by one researcher, but questionable articles were discussed [46].”
BackgroundAccounting for common method variance in cross-sectional research designs
Lindell, M. K., & Whitney, D. J. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.1.114 · Cited by 8,490
Mixing Qualitative and Quantitative Methods: Triangulation in Action.
Jick, T. D. (1979) · Administrative Science Quarterly · doi:10.2307/2392366 · Cited by 7,103 · cited 1×
“in the research findings [43], but the study’s results still suffer from limitations inherent in single method studies [31].”
Result
Cited by
Common method bias: It’ s bad, it’ s complex, it’ s widespread, and it’ s not easy to fix
Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-110721-040030 · Cited by 1,899
Common Method Bias in Public Management Studies
Jakobsen, M., & Jensen, R. (2015) · International Public Management Journal · doi:10.1080/10967494.2014.997906 · Cited by 831
How the use of big data analytics affects value creation in supply chain management
Chen, D. Q., Preston, D. S., & Swink, M. (2015) · Journal of Management Information Systems · doi:10.1080/07421222.2015.1138364 · Cited by 829
Munir, M., Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107667 · Cited by 452
Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2017.10.035 · Cited by 441
Metadata
- Authors
- Craighead, C. W., Ketchen, D. J., Dunn, K. S., & Hult, G. T. M.
- Venue
- IEEE Transactions on Engineering Management
- Published
- 2011-08
- Volume
- 58
- Issue
- 3
- Pages
- 578-588
- ISSN
- 0018-9391 (ISSN-L)
- 1558-0040
- OpenAlex
- W2113814318
- Cited by
- 522
- References
- 69
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- Craighead, C. W., Ketchen, D. J., Dunn, K. S., & Hult, G. T. M. (2011). Addressing Common Method Variance: Guidelines for Survey Research on Information Technology, Operations, and Supply Chain Management. IEEE Transactions on Engineering Management. https://doi.org/10.1109/tem.2011.2136437
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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.
