The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?
Open accessAbstract
This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio‐frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.
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How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability
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Cited by
Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2867 · Cited by 462
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024) · Sustainable Development · doi:10.1002/sd.2773 · Cited by 420
Richnák, P. (2022) · Logistics · doi:10.3390/logistics6040079 · Cited by 28
Metadata
- Authors
- Chiarini, A.
- Venue
- Business Strategy and the Environment
- Publisher
- Wiley
- Published
- 2021-04-10
- Volume
- 30
- Issue
- 7
- Pages
- 3194-3207
- ISSN
- 0964-4733 (ISSN-L)
- 1099-0836
- OpenAlex
- W3156471857
- Cited by
- 213
- References
- 7
- FWCI
- 15.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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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