• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Foresight tools for the study of digital transformation processes in the economy and the society

Priority areas of development: economics, state and public administration, engineering science
2018
The project has been carried out as part of the HSE Program of Fundamental Studies.

The economic diversification, wide implementation of technological advancements and industrial modernization are closely connected with the transition to advanced digital smart manufacturing that is coupled with increased labor productivity, efficient resource use and gradual introduction of international standards. Among the main consumers of digital services are banks, trading and telecommunication companies and the government sector.

The present study is aimed at the analysis of foresight instruments and advancement of foresight methodology in the sphere of digitalization of the economy and the society . In order to attain this goal the following six research tasks were addressed.

1. Advancement of text mining-based methodology and instruments for the evaluation of development drivers in the digital industries.

The research methods include large amounts of unstructured text data analysis (text-mining), trend analysis and forecasting, content analysis, natural language processing, semantic analysis, syntactic analysis, knowledge extraction, named entity recognition, entity linking, clustering and classification, and mapping.

Empirical base of research covers more than two hundred thousand news, patents, research papers, grants, analytical reports covering relevant industrial topics.

As a result of the study the database used for big data analysis was expanded. The instruments to evaluate drivers for digital industries, based on text mining methods, were tested. The methodology of natural language processing was further advanced. All documents and identified terms were classified and grouped in taxonomies. The main areas of digital technologies advancements were determined and visualized in the semantic map. Each of the digital technologies thematic areas were appended with analytical descriptions in the form of tables with key technology trends (terms) that drive the digital development, as well as statistical indicators, on the basis of which these drivers were selected.

The study outcomes may be used as tools for identification of key technology trends in any industry for the purposes of science and technology agenda setting and policy-making. The outcomes may also be used for sectoral/industry-level forecasting (foresight) and subsequent decision-making. Moreover, the research results provide evidence for the identification of prospective digital technology topics and the technologies, for which research and development (R&D) investment could yield higher returns.

2. Development of methods and approaches to digital transformation of traditional sectors of economy (with the example of machine engineering).

The research methods include desk research, analysis of scientific publications, comparative analysis, systematization method, statistical analysis, and case study.

Empirical base of research covers statistical data from international manufacturing and industrial rankings, scientific publications, analytical and industry reports on digitalization, strategic and policy documents related to innovation, science and technology policy, standardization documents.

As a result of the study current conceptual approaches of digital transformation in manufacturing were analyzed. The assessment and systematization of the international best practices of integrating digital technologies in manufacturing were provided. Along with this, based on selected cases, the experience of Russian manufacturing enterprises in digitalization was analyzed. This allowed formulating the main recommendations for application of digital technologies by Russian companies, taking into consideration the specific features of industries and their current standing. More specifically, the need to develop a strategy for digital transformation of manufacturing was identified, along with the need to intersectoral and interagency cooperation in the course of digital agenda implementation, measures for broader inclusion of small and medium-sized companies, as well as extension of public-private partnership.

The suggested recommendations may be used in the development and implementation of national digital manufacturing projects of Russian enterprises, including the machine building industry. Study outcomes may also be useful for state-owned and private companies in order to build corporate models for technological upgrade based on ICT solutions.

3. Identification of priority areas for research and technology development for the housing and utilities sector.

The research methods include analysis of documents (applied policy analysis), literature review, and case study.

Empirical base of research covers Statista — the statistics portal, official cities portals of Charlotte, Barcelona, Shanghai, Tokyo.

As a result of the analysis of research publications, significant elements of the smart city concept were identified and systematized. The concept is grounded in the wide application of information technologies and other integrated solutions. Based on the analysis of international experience of the selected smart cities, proposals for the research and technology development in housing and utilities sector were prepared in the following areas: electricity supply; water supply and sanitation; heating; solid waste disposal.

The study outcomes may be used by Russian regions for the development of long-term development plans in line with the smart city concept. The attention to R&D areas and innovative technologies that were identified as priorities for the housing and utilities sector is one of the main steps towards the transition towards smart cities.

4. Identification of science and technology prerequisites and economic benefits from the introduction of the Internet of Energy.

The research methods include analysis of documents (applied policy analysis), literature review, and case study.

Empirical base of research covers research publications related to the Internet of Energy, Russian regulatory and legislative acts and initiatives pertaining to the Internet of Energy, corporate documents and press-releases developed by international and Russian companies that specialize in the Internet of Energy technologies, products and services.

The outcomes of the study allowed for the development of science and technology, and energy policy recommendations that may allow maximizing the inter-industry and inter-sectoral effects with wide application of certain Internet of Energy technologies and products. Overall, the suggested policy tools may be divided into three big blocks: development of strategic documents, overcoming legislative and administrative barriers, and the support to pilot projects. As many industries and their segments are involved in the development of Internet of Energy technologies and products, it is necessary to establish new approaches to technology regulation and standardization that would correspond to the future energy system. Moreover, the creation of a common energy ecosystem requires interpectoral strategies.

The results of this study can be practically used during the design of projects formed under the auspices of the National Technology Initiative “Energy.Net”, as well as by the Russian companies for assessing the prospects of new products and technologies at “Energy.Net” markets. Moreover, the outcomes may be useful for Russian government agencies involved in regulation of this inter-industry segment.  

5. Development of the methodological framework for long-term forecasting of the scientific and technological indicators in the financial sector considering the impact of economy digitization.

The research methods included long-term forecasting of scientific and technological indicators, analysis of science and technology (S&T) aspects in the financial sector, simulation modelling, regression analysis, scenario analysis, and cluster analysis.

Empirical base of research covers the Central Database of Statistical Data (CDSD) and the Unified Interdepartmental Statistical Information System (UISIS) of the Russian Federal State Statistics Service (Rosstat); statistical database of the Central Bank of the Russian Federation (Bank of Russia); statistical database of Federal Customs Service of the Russian federation; data derived from statistical digests prepared by the Institute for Statistical Studies and Economics of Knowledge at National Research University — Higher School of Economics (ISSEK HSE); data derived from information and analytical system  “Statistics and monitoring the knowledge economy: science, innovation, education, information society” developed jointly by ISSEK HSE and “Prognoz” company; OECD.stat database; OECD Science and technology Indicators; OECD Structural Analysis Database (OECD STAN); UNESCO Institute for Statistics (UIS) Science, Technology and Innovation (full dataset); World input output database (WIOD); World KLEMS;

Research results include the list and description of methods applied for the long-term science and technology forecasting in the financial sector; development of a model for long-term forecasting of scientific and technological indicators for the financial sector; current and future assessment of prospective markets associated with digital technologies; current and future estimation of the digitalization impact on economic growth.

These results can be used by the relevant ministries and other government agencies for drafting long-term science and technology programs for the Russian financial sector.

6. Analysis andan evaluation of foresightstudies for smart transformation processes in economy and society.

The study’s methods include retrospective analysis of the experience gained in the course of the 20-years implementation of five cycles of one of the largest national priority setting and critical technologies research projects (implemented since 1995). The analysis was focused on initially expected and actually achieved effects and lessons learned that would contribute to further advancement of approaches to selecting science and technology priority areas and critical technologies, and their more efficient application. The key research methods areanalysing project documentation and reports and interviewing project team members.

Empirical base of research covers publications connected to the project, terms of reference and project’s reports

The study’s results include the identification of key factors for the successful selection and application of science and technology priorities. Among them are analysing the National Innovation System’s readiness to follow these priorities and implement them; linking S&T priority selection to assessing their potential in addressing the social and economic objectives; adopting an integrated approach, i.e. supplementing thematic S&T priorities with infrastructural and functional ones.

The study’s results are aimed at increasing effectiveness of national S&T priority setting and theirimplementation along with critical technologies. The outcomes could be used by government agencies at federal and regional levels for the development of S&T and innovation policy.

Publications:


Brandão Fischer B., Queiroz S., Vonortas N. On the location of knowledge-intensive entrepreneurship in developing countries: lessons from São Paulo, Brazil // Entrepreneurship & Regional Development. 2018. Vol. 30. No. 5-6. P. 612-638. doi
Kutsenko E., Islankina E., Kindras A. Smart by Oneself? An Analysis of Russian Regional Innovation Strategies within the RIS3 Framework // Foresight and STI Governance. 2018. Vol. 12. No. 1. P. 25-45. doi
Linton J. Quiet Contributors: The Role of the Arts, Humanities and Social Sciences in Innovation // Foresight and STI Governance. 2018. Vol. 12. No. 3. P. 6-12. doi
Calof J. L. Enhancing Innovation Performance in Companies // Foresight and STI Governance. 2018. Vol. 12. No. 3. P. 30-33. doi
Березной А. В. Транснациональный бизнес в эпоху глобальной цифровой революции // Мировая экономика и международные отношения. 2018. Т. 62. № 9. С. 5-17. doi
de Moraes Silva D. R., Furtado A., Vonortas N. University-industry R&D cooperation in Brazil: a sectoral approach // The Journal of Technology Transfer. 2018. Vol. 43. No. 2. P. 285-315. doi
Calof J. L., Arcos R., Sewdass N. Competitive intelligence practices of European firms // Technology Analysis & Strategic Management. 2018. Vol. 30. No. 6. P. 658-671. doi
Aldieri L., Kotsemir M. N., Vinci C. P. Knowledge Spillover Effects: Empirical Evidence from Russian regions // Quality and Quantity. 2018. Vol. 52. No. 5. P. 2111-2132. doi
Fischer B., Schaeffer P., Vonortas N., Queiroz S. Quality comes first: university-industry collaboration as a source of academic entrepreneurship in a developing country // The Journal of Technology Transfer. 2018. Vol. 43. No. 2. P. 263-284. doi
Proskuryakova L. N. Updating energy security and environmental policy: energy security theories revisited // Journal of Environmental Management. 2018. Vol. 223. P. 203-214. doi
Aldieri L., Kotsemir M. N., Vinci C. P. The impact of research collaboration on academic performance: An empirical analysis for some European countries // Socio-Economic Planning Sciences. 2018. Vol. 62. P. 13-30. doi