NEW Business models for ITS
The NEWBITS project is funded by European Union’s Horizon 2020 research and innovation programme. The overall objective is to provide with a deep understanding of the changing conditions and dynamics that affect and/or influence C-ITS innovations. New KPIs will be designed to feed reactive and network oriented value creation propositions that improve the collaborative decision-making process across the various stakeholders. This improved value definition must minimize the failures inherent to C-ITS innovation diffusion, evolve present business models, and identify effective incentives to accelerate C-ITS deployment.
To support its main goal, the project proposes to:
- apply a business ecosystem approach for C- ITS which acknowledges the context of economics of networks by introducing a higher conceptual level than that of individual organisations, focusing at how organisations create value within the context of the networks in which they interoperate;
- design and implement a holistic intelligence process that will gather key information and indicators on market, demand, stakeholder’s involvement and innovation diffusion for ITS and C-ITS;
- develop a tailored application of the Value Network Analysis (VNA) business modelling method, which enables to model value networks, either internal or external to the organizations acting in the business ecosystems;
- formalise the enhanced understanding of the potential system benefits and fundamental economics of new business models suited to C-ITS in the European context and develop relevant outcomes to support policy measures towards C-ITS deployment.
The NEWBITS partners include Ortelio Ltd (United Kingdom) – project coordinator, Universitat Autonoma de Barcelona (Spain), CE Delft (Netherlands), TTS Italia (Italy), Atos Spain SA (Spain), Coventry University Enterprises Ltd (United Kingdom), Steinbeis Innovation gGmbH (Germany), INTELSPACE Innovation Technologies (Greece), Istituto di Studi per l’Integrazione dei Sistemi (Italy).
NEWBITS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 723974.
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