Σεμινάριο με θέμα 'Diamond in the rough: Quantifying failed innovative endeavors'

Διεξαγωγή
Ημερ/νίες
27/09/2022'
'
Τοποθεσία Webinar, Ηράκλειο

Το Γραφείο Μεταφοράς Τεχνογνωσίας (ΓΜΤ), σε συνεργασία με το Μεταπτυχιακό του ΓΜΤ στην καινοτομία και την επιχειρηματικότητα ΤΙΜΕ-ΜΒΕ, και την ερευνητική ομάδα TECHNIS διοργανώνουν την Τρίτη 27 Σεπτεμβρίου στις 15:00 το 1ο στην σειρά σεμινάριο του Γραφείου για την περίοδο 2022-2023.


Ο ομιλητής είναι ο κος Zhen Lei, από το Penn State University.
Ο τίτλος της παρουσιάσεως είναι "Diamond in the rough: Quantifying failed innovative endeavors".

Για να συνδεθείτε ακολουθήστε τον κάτωθι σύνδεσμο: 

https://us02web.zoom.us/j/89840336335?pwd=RE1pUE00Z3NybGZGSUpDTldQa2xxZz09



Abstract:  Many innovative pursuits fail, yet little is known about patterns governing failed innovative endeavors that span scientific and technological domains. Here we analyze large-scale datasets capturing both successful and failed innovative endeavors across three distinct domains, ranging from patent applications to grant proposals to Kickstarter projects. Prior research shows that hits—the successful and high-impact innovations, compared with flops—the successful but low-impact, tend to balance novel combinations of prior knowledge with conventional thinking. Here we propose a simple measure to quantify similar stratifications within failed innovations, allowing us to systematically identify diamonds in the rough (DIRs) and lumps of coal (LOCs). We find that across all three domains, promising but failed ideas strike a similar balance of novelty and conventionality, with DIRs being systematically more novel and more conventional than LOCs. Yet, comparing DIRs with successful innovations, we find that for certain domains, DIRs can be both more novel and conventional than many of the successful innovations, revealing a systematically identifiable population of failed innovations that occupy a similar region as hit innovations on the conceptual space of novelty and conventionality. We devise a toy model that explores the discrepancy between evaluation and recognition processes of an innovation, allowing us to not only explain the hit-DIR proximity, but also identify conditions when such proximity may emerge. Together, these results not only offer a quantitative framework that furthers our understanding of the full spectrum of innovative endeavors—both successful and failed, they may also have implications for identifying and nurturing promising new ideas and technologies, a relevant issue for science and innovation policy.

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