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STI-DUI learning modes, firm-university collaboration and innovation.

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Title: STI-DUI learning modes, firm-university collaboration and innovation.
Authors: González-Pernía, José gonzalez-pernia@deusto.es, Parrilli, Mario, Peña-Legazkue, Iñaki
Source: Journal of Technology Transfer. Jun2015, Vol. 40 Issue 3, p475-492. 18p.
Subjects: Mathematical models of learning, Innovations in higher education, Universities & colleges, Business partnerships, Estimation theory
Abstract: In this study, the relationship between the formation of different types of collaborative agreements and firm innovation is examined. Two types of agreements are distinguished: 'science and technology-based' (STI) and 'learning-by-doing, by-using and by-interacting-based' (DUI) partnerships. A fixed-effects logit estimation is applied on an extensive panel of Spanish manufacturing and service firms to analyze the separate and combined impact of collaborative agreements associated to STI and/or DUI modes of learning, paying special attention to the role of partnerships with universities. While STI and DUI partnerships are important for product and process innovation in both separate and combined ways, the results demonstrate that different types of collaboration lead to different types of innovation. Product innovation benefits more from the combination of DUI and STI partnerships and process innovation seems to be more closely linked to DUI-related partnerships undertaken by firms. Findings also show that firm-university collaboration, without the engagement of other STI and DUI agents in the partnership, seems to be weak for product innovation. [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: STI-DUI learning modes, firm-university collaboration and innovation.
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  Data: <searchLink fieldCode="DE" term="%22Mathematical+models+of+learning%22">Mathematical models of learning</searchLink><br /><searchLink fieldCode="DE" term="%22Innovations+in+higher+education%22">Innovations in higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Universities+%26+colleges%22">Universities & colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Business+partnerships%22">Business partnerships</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
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  Data: In this study, the relationship between the formation of different types of collaborative agreements and firm innovation is examined. Two types of agreements are distinguished: 'science and technology-based' (STI) and 'learning-by-doing, by-using and by-interacting-based' (DUI) partnerships. A fixed-effects logit estimation is applied on an extensive panel of Spanish manufacturing and service firms to analyze the separate and combined impact of collaborative agreements associated to STI and/or DUI modes of learning, paying special attention to the role of partnerships with universities. While STI and DUI partnerships are important for product and process innovation in both separate and combined ways, the results demonstrate that different types of collaboration lead to different types of innovation. Product innovation benefits more from the combination of DUI and STI partnerships and process innovation seems to be more closely linked to DUI-related partnerships undertaken by firms. Findings also show that firm-university collaboration, without the engagement of other STI and DUI agents in the partnership, seems to be weak for product innovation. [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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              Text: Jun2015
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