Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning.
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| Title: | Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning. |
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| Authors: | Fonseca, Camila Veneo C.1 (AUTHOR) cveneo@unicamp.br, Predo, Renata Martins1 (AUTHOR), Blikstad, Nicholas M. D.1 (AUTHOR) |
| Source: | International Journal of Production Research. Dec2025, Vol. 63 Issue 23, p8743-8760. 18p. |
| Subjects: | Industry 4.0, Sustainability, Inquiry (Theory of knowledge), Network theory (Statistical physics), Business planning, Technological innovations, Machine learning |
| Abstract: | While there is broad consensus on the importance of incorporating Industry 4.0 technologies into companies' present and future strategies, establishing a common conceptual framework in manufacturing research remains challenging. This paper addresses whether such a framework exists and explores the potential of Industry 4.0 technologies to enhance managerial strategies for companies. The paper then organises the diverse literature on Industry 4.0 technologies to achieve two objectives: identifying a common conceptual framework across various approaches and proposing a research agenda for integrating these technologies into managerial strategies. To achieve these objectives, we employ novel methodologies to identify relevant emerging topics: scientometric analysis, network analysis, and Structural Topic Model (STM). The results reveal a greater alignment in the conceptual framework and in the identification of the most important technologies within it in recent years. Additionally, manufacturing research is increasingly exploring the intersection of Industry 4.0, managerial strategies and sustainability, opening a promising research agenda focused on applications and implications for technological transition and value creation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189849833 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fonseca%2C+Camila+Veneo+C%2E%22">Fonseca, Camila Veneo C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cveneo@unicamp.br</i><br /><searchLink fieldCode="AR" term="%22Predo%2C+Renata+Martins%22">Predo, Renata Martins</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Blikstad%2C+Nicholas+M%2E+D%2E%22">Blikstad, Nicholas M. D.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Dec2025, Vol. 63 Issue 23, p8743-8760. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Inquiry+%28Theory+of+knowledge%29%22">Inquiry (Theory of knowledge)</searchLink><br /><searchLink fieldCode="DE" term="%22Network+theory+%28Statistical+physics%29%22">Network theory (Statistical physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Business+planning%22">Business planning</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: While there is broad consensus on the importance of incorporating Industry 4.0 technologies into companies' present and future strategies, establishing a common conceptual framework in manufacturing research remains challenging. This paper addresses whether such a framework exists and explores the potential of Industry 4.0 technologies to enhance managerial strategies for companies. The paper then organises the diverse literature on Industry 4.0 technologies to achieve two objectives: identifying a common conceptual framework across various approaches and proposing a research agenda for integrating these technologies into managerial strategies. To achieve these objectives, we employ novel methodologies to identify relevant emerging topics: scientometric analysis, network analysis, and Structural Topic Model (STM). The results reveal a greater alignment in the conceptual framework and in the identification of the most important technologies within it in recent years. Additionally, manufacturing research is increasingly exploring the intersection of Industry 4.0, managerial strategies and sustainability, opening a promising research agenda focused on applications and implications for technological transition and value creation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2025.2514253 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 8743 Subjects: – SubjectFull: Industry 4.0 Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Inquiry (Theory of knowledge) Type: general – SubjectFull: Network theory (Statistical physics) Type: general – SubjectFull: Business planning Type: general – SubjectFull: Technological innovations Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fonseca, Camila Veneo C. – PersonEntity: Name: NameFull: Predo, Renata Martins – PersonEntity: Name: NameFull: Blikstad, Nicholas M. D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 23 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
