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A meta‐analysis model of manufacturing capabilities

White, Gregory P.·1996·Journal of Operations Management

Abstract

Recent research in operations strategy has focused on showing the extent to which manufacturing's competence in developing and executing one or more competitive capabilities affects the organization's overall business performance. Several of those efforts have produced models, such as the ‘sand cone’, that suggest an ideal sequence in which manufacturing capabilities should be developed. However, efforts to empirically validate such sequential models, except for one recent study, have been inconclusive. This paper takes a different approach to examining the relationships among competitive capabilities. The existing cross‐sectional studies of manufacturing capabilities are integrated to synthesize a proposed model of: (1) the relationships among manufacturing capabilities, and (2) the relationships between those capabilities and business performance. The proposed model is synthesized using data from previous studies, other models and theoretical arguments. Meta‐analysis is used to identify aspects of the model that appear better supported by empirical research and those that require further study. The implications of this proposed model with regard to research, practice and future research in operations strategy are discussed.

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Metadata

Authors
White, G. P.
Venue
Journal of Operations Management
Publisher
Wiley
Published
1996-11
Volume
14
Issue
4
Pages
315-331
ISSN
  • 0272-6963 (ISSN-L)
  • 1873-1317
OpenAlex
W1982606489
Cited by
183
References
1
FWCI
1.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A meta‐analysis model of manufacturing capabilities. https://app.cochat.ai/papers/10.1016/s0272-6963(96)00096-4
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White, G. P. (1996). A meta‐analysis model of manufacturing capabilities. Journal of Operations Management. https://doi.org/10.1016/s0272-6963(96)00096-4

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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.