The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
Advanced manufacturing technology adoption and innovation: A systematic literature review on barriers, enablers, and innovation types
Abstract
Abstract This study examines the barriers and enablers within the adoption process of advanced manufacturing technologies (AMTs) and their relationship to different innovation types as outcomes of this process. Whilst the requirement to improve AMT adoption is widely acknowledged, the existing literature lacks a comprehensive understanding of the main barriers and enablers within the adoption process. Furthermore, it does not reveal how these barriers and enablers are related to different innovation types. We provide a systematic literature review of the barriers and enablers to AMT adoption. The findings of this review are based on a total of eighty-seven peer-reviewed articles from the business and management literature from 1999-2019. Our findings provide several contributions to the literature. Firstly, by integrating evidence from empirical studies, we identify five main types of barriers and four main types of enablers to adoption. Secondly, we conceptualise three key stages of the AMT adoption process and classify the barriers and enablers on the basis of whether they apply to an individual stage, or whether their influence is pertinent across the whole process. Thirdly, we reveal the relationship between categories of barriers and enablers associated to innovation types outcomes: product, process, service or business model innovations. The results of our study provide important implications for both managers and policy makers based on the evidence of the key barriers and enablers. Further, we suggest several directions for future research, including the need to examine the post-installation stage.
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Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · An Integrated Approach to Communication Theory and Research · doi:10.4324/9780203710753-35 · Cited by 37,322
Tranfield, D., Denyer, D., & Smart, P. (2003) · British Journal of Management · doi:10.1111/1467-8551.00375 · Cited by 15,386
Valente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 13,723
Danaei, G., Ding, E. L., Mozaffarian, D., Taylor, B., Rehm, J., Murray, C. J. L., & Ezzati, M. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000058 · Cited by 2,860
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
Additive manufacturing and sustainability: an exploratory study of the advantages and challenges
Ford, S., & Despeisse, M. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.04.150 · Cited by 1,889
Teece, D. J. (2018) · Research Policy · doi:10.1016/j.respol.2017.01.015 · Cited by 1,767
Cited by
Gama, F., & Magistretti, S. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12698 · Cited by 388
Metadata
- Authors
- Stornelli, A., Ozcan, S., & Simms, C.
- Venue
- Research Policy
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 50
- Issue
- 6
- Pages
- 104229
- ISSN
- 0048-7333 (ISSN-L)
- OpenAlex
- W3135083964
- Cited by
- 268
- References
- 111
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Stornelli, A., Ozcan, S., & Simms, C. (2021). Advanced manufacturing technology adoption and innovation: A systematic literature review on barriers, enablers, and innovation types. Research Policy. https://doi.org/10.1016/j.respol.2021.104229
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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