The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective
Open accessAbstract
This paper examines barriers to the implementation of Industry 4.0 technologies in the manufacturing sector in the context of both developed and developing economies. A comprehensive literature review, followed by discussions with industry experts, identifies 15 barriers, which are analyzed by means of a Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The ‘lack of a digital strategy alongside resource scarcity’ emerges as the most prominent barrier in both developed and developing economies. The influencing barriers identified suggest that improvements in standards and government regulation could facilitate the adoption of Industry 4.0 technologies in developing country case, whereas technological infrastructure is needed to promote the adoption of these technologies in developed country case. This study is one of the first to examine the implementation of Industry 4.0 in both developing and developed economies. This article highlights the difficulties in the diffusion of technological innovation resulting from a lack of coordinated national policies on Industry 4.0 in developing countries, which may prevent firms from fully experiencing the Industry 4.0 revolution. The results of this study may help decision makers and practitioners to address the barriers highlighted, paving the way for successful implementation of Industry 4.0 across the manufacturing sector.
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Citations
Cites
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012
The productivity paradox of information technology
Brynjolfsson, E. (1993) · Communications of the ACM · doi:10.1145/163298.163309 · Cited by 2,604
Ivanov, D., Dolgui, A., & Sokolov, B. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1488086 · Cited by 2,055
Internet of things and supply chain management: A literature review
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019) · International Journal of Production Research · doi:10.1080/00207543.2017.1402140 · Cited by 1,501
Lopes de Sousa Jabbour, A. B., Jabbour, C. J. C., Godinho Filho, M., & Roubaud, D. (2018) · Annals of Operations Research · doi:10.1007/s10479-018-2772-8 · Cited by 1,116
Luthra, S., & Mangla, S. K. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.04.018 · Cited by 909
Big Data: Survey, Technologies, Opportunities, and Challenges
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014) · The Scientific World Journal · doi:10.1155/2014/712826 · Cited by 547
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Past, present, and future barriers to digital transformation in manufacturing: A review
Jones, M. D., Hutcheson, S., & Camba, J. D. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.006 · Cited by 377
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P. K., Albores, P., & Emrouznejad, A. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121562 · Cited by 279
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Bettoni, A., Matteri, D., Montini, E., Gładysz, B., & Carpanzano, E. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.082 · Cited by 124
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Calabrese, A., Dora, M., Levialdi Ghiron, N., & Tiburzi, L. (2022) · Production Planning & Control · doi:10.1080/09537287.2020.1830315 · Cited by 109
Amin, A., Bhuiyan, M. R. I., Hossain, R., Molla, C., Poli, T. A., & Milon, M. N. U. (2024) · Business Strategy & Development · doi:10.1002/bsd2.363 · Cited by 86
Dyba, W., Di Maria, E., & Chiarvesio, M. (2022) · Investigaciones Regionales - Journal of Regional Research · doi:10.38191/iirr-jorr.22.009 · Cited by 16
DT4I4-Secure: Digital Twin Framework for Industry 4.0 Systems Security
Lin, Y.-Z., Shao, S., Rahman, M. H., Shafae, M., & Satam, P. (2023) · 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316090 · Cited by 14
Hosseini Dehshiri, S. J., & Amiri, M. (2025) · Energy Strategy Reviews · doi:10.1016/j.esr.2025.101761 · Cited by 10
Metadata
- Authors
- Raj, A., Dwivedi, G., Sharma, A., Lopes de Sousa Jabbour, A. B., & Rajak, S.
- Venue
- International Journal of Production Economics
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 224
- Pages
- 107546
- ISSN
- 0925-5273 (ISSN-L)
- OpenAlex
- W2986257753
- Cited by
- 1,042
- References
- 7
- FWCI
- 66.34
- Type
- journal article
- Language
- en
- Open access
- green
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