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Lunchtime seminar- Regional branching and Artificial Intelligence: an empirical analysis of technological specialization in European provinces

International Business and Strategy Lunchtime Seminar Series

Presenter- Elettra D’Amico, PhD student at Politenico di Torino and Visiting PhD at Henley

Henley Business School outside
Event information
Date 4 May 2022
Time 13:00-14:00 (Timezone: Europe/London)
Price Free
Venue Henley Business School
Event types:
Seminars

You are cordially invited to attend an IBS lunchtime research seminar by Elettra D’Amico, PhD student at Politenico di Torino and Visiting PhD at Henley. Please join us in Room 108, HBS or join via your Microsoft Teams Meeting link sent via invite email.

If you have not received the invite email please email Ellie Biggs.

Please note: Lunch and refreshments will be provided. It is important that you confirm if you are attending in-person to assure enough catering is supplied on the day. If you have any dietary restrictions, please let us know as soon as possible.

Date: Wednesday 4th May 2022

Time: 13.00-14.00pm

Title: Regional branching and Artificial Intelligence: an empirical analysis of technological specialization in European provinces

Presenter: Elettra D’Amico, PhD student at Politenico di Torino in Management, Production and Design and Visiting PhD at Henley Business School.

Co-authors- Professor Alberto De Marco (Politenico di Torino) and Dr Alessandra Colombelli (Collegio Carlo Alberto)

Room: 108, HBS

Abstract:

This paper aims to establish the role played by Artificial Intelligence (AI), as a key enabling technology (KET), in regional branching. Considering its general-purpose properties, we investigate the effect that AI may have on developing new technological specializations in European provinces. The objective of the paper is to analyze the impact of local availability of AI-related knowledge and competencies – measured via patents– on the subsequent evolution in technological specialization. The empirical analysis is based on a database collecting all patents filed at the European Patent Office (EPO) from 1980 to 2018 and geo-localized at the NUTS3 level. We implement an original methodology to identify AI patents that rely on the presence of specific technological codes and keywords (WIPO, 2019). Our preliminary results provide support for regional branching policies and the management of innovation.

Keywords: Artificial Intelligence, Regional Branching, Revealed Technology Advantage, Innovation, Relatedness, Patents

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