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Measuring the impacts of university-industry R&D collaborations: a systematic literature review.

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Title: Measuring the impacts of university-industry R&D collaborations: a systematic literature review.
Authors: Cohen, Maria1 (AUTHOR) martinezcohen@student.fe.uc.pt, Fernandes, Gabriela2 (AUTHOR) gabriela.fernandes@dem.uc.pt, Godinho, Pedro1 (AUTHOR) pgodinho@fe.uc.pt
Source: Journal of Technology Transfer. Feb2025, Vol. 50 Issue 1, p345-374. 30p.
Subjects: Authorship collaboration, Research & development projects, Academia
Abstract: Measuring the impacts of collaborative projects between industry and academia raises significant challenges. It involves stakeholders with different outlooks and impact expectations. Moreover, the multidimensional nature of the impacts themselves means they are tangible and intangible, short- and long-term, direct and indirect, positive and negative, making their measurement process very complex. To gain a deeper understanding of how university-industry R&D collaborations (UICs) impact society, this study conducts a systematic review, using thematic analysis of 92 selected articles published between 2000 and 2022. The paper identifies and categorizes the impacts resulting from UICs, examines the challenges associated with measuring these impacts, and explores the strategies that can be employed to overcome such challenges. Finally, the paper integrates all such findings into a comprehensive framework. This study contributes to the theoretical advancement of impact measurement within the field of UICs, providing a foundation for the development of methodologies aimed at assessing impacts. Furthermore, it highlights important avenues for future research. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Technology Transfer is the property of Springer Nature 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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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Technology+Transfer%22">Journal of Technology Transfer</searchLink>. Feb2025, Vol. 50 Issue 1, p345-374. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Authorship+collaboration%22">Authorship collaboration</searchLink><br /><searchLink fieldCode="DE" term="%22Research+%26+development+projects%22">Research & development projects</searchLink><br /><searchLink fieldCode="DE" term="%22Academia%22">Academia</searchLink>
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  Data: Measuring the impacts of collaborative projects between industry and academia raises significant challenges. It involves stakeholders with different outlooks and impact expectations. Moreover, the multidimensional nature of the impacts themselves means they are tangible and intangible, short- and long-term, direct and indirect, positive and negative, making their measurement process very complex. To gain a deeper understanding of how university-industry R&D collaborations (UICs) impact society, this study conducts a systematic review, using thematic analysis of 92 selected articles published between 2000 and 2022. The paper identifies and categorizes the impacts resulting from UICs, examines the challenges associated with measuring these impacts, and explores the strategies that can be employed to overcome such challenges. Finally, the paper integrates all such findings into a comprehensive framework. This study contributes to the theoretical advancement of impact measurement within the field of UICs, providing a foundation for the development of methodologies aimed at assessing impacts. Furthermore, it highlights important avenues for future research. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Technology Transfer is the property of Springer Nature 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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        Value: 10.1007/s10961-024-10114-5
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        Text: English
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              Text: Feb2025
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