The action ‘Smart Specialization Strategy Tools with Big Data’ aims to integrate big data methods in the analysis and management of European Smart Specialization Strategy (S3). S3 is a key concept in European regional economic development. In particular, it is an innovation policy that aims to identify promising economic areas in a region for investment and specialization. In setting innovation-policy priorities, S3 relies on Entrepreneurial Discovery Process (EDP) in which local actors from business and academia find the right areas of future specialization by discovering new market niches as well as scientific and technological opportunities. The role of government in this process is to identify those entrepreneurial discovery projects or new activities and to build critical mass in promising areas of specialization. While this process, in turn, requires a deep analysis of local capabilities and competencies to identify unique features and strengths of each region and, based on this, to set innovation policy priorities, policy-makers lack efficient and viable tools for mapping promising activities for smart specialization. This MSCA project SSST-BD engages to propose text mining and machine learning methods for the design and planning of this strategy.
This topic is important for society because the expected advancement within this field will play an important role to boost the innovation and competitiveness potential of the European regions. By employing interdisciplinary elements of economic innovation, foresight and big data fields, the project aims to show how big data methods and analytics can be used to identify and assess entrepreneurial discovery processes and to design S3. The research outcomes of the project also help regional governments and companies to test and evaluate new business concepts and assess patent values based on weak signals of technological changes. Firms’ innovation and competitiveness are determined by how quickly and effectively an organization is able to exploit new business opportunities and cope with future threats. In this context, future-oriented thinking is required to anticipate and respond socio-economic and technological changes. The methods the project propose can help firms and regions to understand complex innovation ecosystems in dynamic VUCA (volatility, uncertainty, complexity and ambiguity) environment and to manage their science, innovation, technology and investment policy.
Objectives of this Marie Skłodowska Curie Action (MSCA) have been to incorporate technology foresight in the planning of S3 and to measure the competitiveness of economic and entrepreneurial activities within/across regions. A parallel goal of the MSCA is to foster the development of the individual researcher.
