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Do AI start-ups perform better than others? Evidence from the Italian startup ecosystem

Francesco Cecchetto

Do AI start-ups perform better than others? Evidence from the Italian startup ecosystem.

Rel. Alessandra Colombelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2020

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This thesis has been conceived to monitor the Italian startups’ situation and how entrepreneurship is evolving inside the county. The focal scope is to study the diffusion of this network in order to decide if the country’s measure taken to promote the born of a new company and to boost the creation of innovation are performing as desired. Especially the core of the research is to understand if the effects are different for a different type of focus of the companies. What I wanted to answer to was “are startups based on AI performing better than other ones? And on what metrics?” The thesis is divided into two major parts: the first one based on literature with the aim of understanding better the startup world and the metrics that are used to evaluate the startup’s performances. In this part will also be analyzed also what AI means and its relationship with startups. In the second part instead, I pass to analyze the data. These are taken making a matching between the database on innovative startups given by the Italian Government and the database on startups extracted from AIDA. I will present some descriptive analysis of the startup situation and diffusion in Italy, always distinguishing between AI- based and other startups. The final step of the analysis will look at the performance metrics identified as relevant and search for a significative difference in values between the AI- based startups and the control group. The idea in creating this thesis is to understand how AI drives the performance and if this indicator could be a predictor of superior ones. It could be useful for all the stakeholders interested in startups’ business.

Relators: Alessandra Colombelli
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 84
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/16475
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