The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union
Abstract
Abstract Industry 4.0 is a concept that represents the adoption by industrial companies of techniques and processes allowed by digitization, cloud computing, the internet of things and big data to gain competitive advantages in domestic and global markets. Measuring how the manufacturing sector is adopting Industry 4.0 is challenging, given that there is not a closed definition of the term and that the collection of information is not specifically directed to Industry 4.0 concepts. Recognizing these difficulties, the present study measures the presence of the factors that characterize Industry 4.0 in manufacturing across EU countries. The analysis provides evidence that the existence of a digital infrastructure combined with the analytical capabilities to deal with big data emerge as the two dimensions that show the readiness for Industry 4.0 in each country. At EU level, five homogeneous groups of countries were found, showing large disparities across countries. The implementation of the single digital market strategy with regard to the manufacturing sector demands the collection of more detailed information on Industry 4.0.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The industrial management of SMEs in the era of Industry 4.0
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1372647 · Cited by 1,188
A big data system supporting Bosch Braga Industry 4.0 strategy
Santos, M. Y., Oliveira e Sá, J., Andrade, C., Vale Lima, F., Costa, E., Costa, C., Martinho, B., & Galvão, J. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.07.012 · Cited by 207
Research works printed but not linked: 28
- Bleicher, 2016, Digitization as a catalyst for business model innovation a three-step approach to facilitating economic success, J. Bus. Manag., 8, 62
- Grover, 2013, Revealing your hand: caveats in implementing digital business strategy, MIS Q., 37, 655
- Ashrafi, 2015, Delineating IT resources and capabilities to obtain competitive advantage and improve firm performance, Inf. Syst. Manag., 32, 15, 10.1080/10580530.2015.983016 · DOI
- Mithas, 2013, How a firm’s competitive environment and digital strategy posture influence digital business strategy, MIS Q., 37, 511, 10.25300/MISQ/2013/37.2.09 · DOI · Paper page
- Dery, 2017, The digital workplace is key to digital innovation, MIS Q. Exec., 16, 135
- Dongback, 2017, Digital business convergence and emerging contested fields: a conceptual framework, J. Assoc. Inf. Syst., 18, 687
- Hofmann, 2017, Industry 4.0 and the current status as well as future prospects on logistics, Comput. Ind., 89, 23, 10.1016/j.compind.2017.04.002 · DOI · Paper page
- Qin, 2016, A categorical framework of manufacturing for Industry 4.0 and beyond, Procedia CIRP, 10.1016/j.procir.2016.08.005 · DOI · Paper page
- Hermann, 2016, Design principles for industrie 4.0 scenarios, Proc. Annu. Hawaii Int. Conf. Syst. Sci., 2016, 3928
- Brettel, 2014, How virtualization, decentralization and network building change the manufacturing landscape: int, J. Inf. Commun. Eng., 8, 37
- Boyes, 2018, The industrial internet of things (IIoT): an analysis framework, Comput. Ind., 101, 1, 10.1016/j.compind.2018.04.015 · DOI · Paper page
- Agarwal, 2015, Strategic business transformation through technology convergence: implications from General Electric’s industrial internet initiative, Int. J. Technol. Manag., 67, 196, 10.1504/IJTM.2015.068224 · DOI
- Reischauer, 2018, Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing, Technol. Forecast. Soc. Change, 132, 26, 10.1016/j.techfore.2018.02.012 · DOI · Paper page
- Morgan, 2015, The cyber-physical implementation of cloud manufacturing monitoring systems, Procedia CIRP, 33, 29, 10.1016/j.procir.2015.06.007 · doi:10.1016/j.procir.2015.06.007
- Mosterman, 2016, Industry 4.0 as a cyber-physical system study, Softw. Syst. Model, 15, 17, 10.1007/s10270-015-0493-x · DOI · Paper page
- Chiarello, 2018, Extracting and mapping industry 4.0 technologies using wikipedia, Comput. Ind., 100, 244, 10.1016/j.compind.2018.04.006 · DOI · Paper page
- Niehaves, 2013, Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide, Eur. J. Inf. Syst. Adv. Online Publ.
- Po, 2008, Understanding digital inequality: comparing continued use behavioral models of the socio-economically advantaged and disadvantaged, MIS Q., 32, 97, 10.2307/25148830 · DOI · Paper page
- Cruz-Jesus, 2012, Digital divide across the European Union, Inf. Manag., 10.1016/j.im.2012.09.003 · DOI · Paper page
- Cruz-Jesus, 2016, Assessing the pattern between economic and digital development of countries, Inf. Syst. Front., 17
- Vicente, 2011, Assessing the regional digital divide across the European Union-27, Telecomm. Policy, 10.1016/j.telpol.2010.12.013 · DOI · Paper page
- El Sawy, 2016, How Lego built the foundations and enterprise capabilities for digital leadership, MIS Q. Exec., 15, 141
- Oesterreich, 2016, Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry, Comput. Ind., 83, 121, 10.1016/j.compind.2016.09.006 · DOI · Paper page
- Wang, 2016, Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination, Comput. Netw., 101, 158, 10.1016/j.comnet.2015.12.017 · DOI · Paper page
- Pedone, 2018, Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies, Comput. Ind., 100, 278, 10.1016/j.compind.2018.05.003 · DOI
- Wang, 2017, Cloud-assisted interaction and negotiation of industrial robots for the smart factory, Comput. Electr. Eng., 63, 66, 10.1016/j.compeleceng.2017.05.025 · DOI
- Peres-Neto, 2005, How many principal components? Stopping rules for determining the number of non-trivial axes revisited, Comput. Stat. Data Anal., 49, 974, 10.1016/j.csda.2004.06.015 · DOI · Paper page
- Bharadwaj, 2013, Digital business strategy: toward a next generation of insights, MIS Q., 37, 471, 10.25300/MISQ/2013/37:2.3 · DOI · Paper page
Cited by
Industry 4.0 and the Circular Economy: A Literature Review and Recommendations for Future Research
Awan, U., Sroufe, R., & Shahbaz, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2731 · Cited by 609
Digital transformation success under Industry 4.0: a strategic guideline for manufacturing SMEs
Ghobakhloo, M., & Iranmanesh, M. (2021) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-11-2020-0455 · Cited by 379
Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0
Kalsoom, T., Ramzan, N., Ahmed, S., & Ur-Rehman, M. (2020) · Sensors · doi:10.3390/s20236783 · Cited by 341
Butt, J. (2020) · Designs · doi:10.3390/designs4030017 · Cited by 247
Brodny, J., & Tutak, M. (2022) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc8020067 · Cited by 187
Metadata
- Authors
- Castelo-Branco, I., Cruz-Jesus, F., & Oliveira, T.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 107
- Pages
- 22-32
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W2914162553
- Cited by
- 456
- References
- 2
- FWCI
- 43.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. https://app.cochat.ai/papers/10.1016/j.compind.2019.01.007
- The paper
- Castelo-Branco, I., Cruz-Jesus, F., & Oliveira, T. (2019). Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. Computers in Industry. https://doi.org/10.1016/j.compind.2019.01.007
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.
