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Business model innovation for data-driven services: the case of 5G connected vehicles

Noopoor Vijay Misal

Business model innovation for data-driven services: the case of 5G connected vehicles.

Rel. Paolo Landoni, Giuliano Sansone, Alessandro Laspia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2021


The past few years have seen an influx of data in all industries. The automotive industry is one such industry which has become more amenable to the use of data and the plethora of opportunities that can be explored by stakeholders and actors. For industry players, the exploitation of the enormous amount of data a vehicle generates can be strategic to increase revenues, reduce costs, improve customer experience, and strengthen safety and security measures. 5GMETA ("5G: Monetizing car and mobility data for new Entrants, Technologies, and Actors") is a project funded by the European Union that intends to create a vehicular data ecosystem in Europe powered by a flexible and modular 5G edge approach. The main goal is to catalyse data-based innovation services for connected and autonomous mobility applications. The results will be the creation of new markets beyond basic data processing and analysis, paving the way to novel paradigms and expanding the horizons of the automotive ecosystem. This work aims to extrapolate the scope of 5GMETA in Europe via a thorough investigation of three diverse concepts viz. 5G mobility, business model innovation, and data-driven services. Furthermore, to determine the possible use cases and to accurately identify the strengths, weaknesses, opportunities, and threats, a concentrated research on the industry and a case study on the 5GMETA platform was carried out. Finally, the work enlightens the reader about the industry disruption that will occur and identifies the types of new data-driven business models that can emerge using real-time data acquired through 5G connectivity. Existing stakeholders and the new entrants can use these business models to leverage vehicle data for certain use cases and benefit from them.

Relators: Paolo Landoni, Giuliano Sansone, Alessandro Laspia
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 161
Additional Information: Tesi secretata. Fulltext non presente
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: FONDAZIONE LINKS
URI: http://webthesis.biblio.polito.it/id/eprint/19408
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