Smart Specialization Strategy Tools with Big Data

Project Information

SSST-BD

Grant agreement ID: 832862

DOI

10.3030/832862

Project closed

EC signature date 19 March 2019

Start date 1 September 2019

End date 3 October 2021

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 190 680,96

EU contribution

€ 190 680,96

Coordinated by

TURUN YLIOPISTO
Finland

Periodic Reporting for period 1 - SSST-BD (Smart Specialization Strategy Tools with Big Data)

Reporting period: 2019-09-01 to 2021-08-31

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Work was conducted via 3 work packages (WPs). In WP1, text mining and network analysis methods are utilized to explore regional entrepreneurial ecosystems and their competitiveness for smart specialization, as well as to map emerging business areas and to identify digitalization trends across industries. WP1 yielded 1 journal publication, 1 conference publication and 2 journal manuscripts underway. Moreover, the content of WP1 were delivered at 2 conferences and 3 workshops. In WP2, the fellow use text mining and machine learning methods to incorporate technology foresight in the planning of S3 and to identify and assess entrepreneurial discovery processes (e.g. startup and invention activities) with weak and strong signals. In it, the fellow produces 2 journal manuscripts and delivered 2 workshop presentations. WP3 involves detecting emerging technological topics from patent data and exploring potential specialization opportunities across European regions, as well as studying data-driven smart specialization strategy with export-import data analytics and also examining how the formal and informal aspects of coopetition influence on innovation activities in emerging markets. Under WP3, the fellow delivered 3 manuscripts: 2 manuscript forthcomings at Open Research Europe Platform and 1 journal manuscript underway. Moreover, 2 conference and 1 workshop presentations are delivered in WP3. Furthermore, during MSCA project, the fellow undertook training and specialized in big data management and analytics, as well as mentoring and advising PhD students regarding Big Data, Industry 4.0 and S3 research.

Results of this MSCA are reported in: (1) forthcoming paper on exploring regional entrepreneurial ecosystems and their competitiveness for smart specialization; (2) journal article on mapping the wave of industry digitalization by co-word analysis; (3) conference publication and manuscript on digitalization and innovation activities; (4) forthcoming papers on identifying and assessing entrepreneurial discovery processes (e.g. startup and innovation activities) with technology weak and strong signals; (5) forthcoming paper on topic-based technological forecasting for the design and planning of European S3; (6) forthcoming paper on data-driven smart specialization strategy with export-import data analytics; and (7) forthcoming paper on the role of coopetition in service innovation in emerging markets.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project SSST-BD proposes big data methods to facilitate evidence-based policymaking and to promote sustainable and inclusive growth across European regions. Specifically, this MSCA suggests big data methods to identify regional entrepreneurial activities and to measure their competitiveness for smart specialization, as well as to link weak signals with new business concepts and invention activities. As regional governments and many companies face problems to process massive amounts of external information, interpret signals of technological changes and use them in strategic management and innovation policymaking, the proposed text mining and machine learning approaches can be used to combine foresight practice with innovation activities, to scan external environment, to identify new technological opportunities and to assess patent values, as well as to detect startups with the promising business concepts among the enormous number of new entrants. The proposed methods can be also used to identify potential products and services for the future across different territories and to map weak signal related startup businesses as well as to reveal from which regions weak signals come from and to determine potential future specialization opportunities. The project SSST-BD has demonstrated new methodological opportunities to investigate innovation potentials and hidden comparative advantages of the European Union and member countries and regions. The results are promising and require the keen attention of the European Commission. The general policy recommendation is that European Smart Specialization Strategy should be supported with Big Data analytics and investigations. The SSST-BD project provides new insights to data-driven STI policy and analysis in the European Union.

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