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Later weeks will focus on developing a robust theoretical framework through which to better understand and interrogate claims about the origin of innovation.
The interest that arose not only from the work of Schumpeter (19501983) on the role of innovation in economic dynamism but also from that of Solow (1957) on residual technical progress and economic growth gave birth to a new analysis aiming at explaining the origin of innovation and of knowledge.
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More generally, our study provides valuable new clues about the origin of innovations in complex adaptive systems.
From this perspective, the origin of innovations is explained as a constellation of collective learning.
The OV framework is a valid method for explaining the origin of innovations in retrospective.
In sum, the OV is aimed at describing the origin of innovations in retrospective.
Given this importance, surprisingly little research has been conducted on the origin of innovations.
The OV, however, does not only allow for an explanation of the origin of innovations in retrospective.
Winter (2012) argues that "serendipity" and surrounding circumstances are the decisive factors for the origin of innovations.
It is only, however, by bringing all three dimensions together, that we can really explain the origin of innovations.
While the model is an adequate method for explaining the origin of innovations in retrospective, its predictive capabilities are limited in this regard.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com