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Implementation of digital manufacturing technologies: Antecedents and consequences
Abstract
Abstract The use of digital manufacturing technologies (DMTs) is changing the face of the manufacturing landscape and enhancing the competitiveness of firms. However, its implementation is still a relatively unexplored phenomenon and demands attention. The purpose of this study is to determine the role played by technological context, organizational context, and environmental context of firms in the implementation of the DMTs. A hypothesized model is proposed and tested using survey-based data from the International Manufacturing Strategy Survey VI comprising 931 firms across developed and developing countries. The proposed research model gains support from the technology–organization–environment framework and the combinatorial technology evolution theory, which together provide a holistic understanding of the technology implementation process in terms of its enablers and performance outcomes. We find that although the organizational context and environmental context impact DMTs' implementation, these impacts are mediated through the technological context of the firm. Moreover, the implementation of these technologies has a significant impact on firm performance in terms of flexibility, design, delivery, and quality performance. Contributing to the empirical research gap in the domain of DMTs’ implementation, this study also generates insights for practitioners about the complementarity of the technological, organizational, and environmental factors that together enable the implementation of DMTs and enhance firm performance.
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Metadata
- Authors
- Gillani, F., Chatha, K. A., Sadiq Jajja, M. S., & Farooq, S.
- Venue
- International Journal of Production Economics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 229
- Pages
- 107748
- ISSN
- 0925-5273 (ISSN-L)
- OpenAlex
- W3021635736
- Cited by
- 209
- References
- 19
- FWCI
- 18.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- This page
- CoChat. (2026). Citation check: Implementation of digital manufacturing technologies: Antecedents and consequences. https://app.cochat.ai/papers/10.1016/j.ijpe.2020.107748
- The paper
- Gillani, F., Chatha, K. A., Sadiq Jajja, M. S., & Farooq, S. (2020). Implementation of digital manufacturing technologies: Antecedents and consequences. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2020.107748
Data sources
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.