Abstract
This study examines the implications of the predicted big data revolution in social sciences for the research using the Triple Helix (TH) model of innovation and knowledge creation in the context of developing and transitional economies. While big data research promises to transform the nature of social inquiry and improve the world economy by increasing the productivity and competitiveness of companies and enhancing the functioning of the public sector, it may also potentially lead to a growing divide in research capabilities between developed and developing economies. More specifically, given the uneven access to digital data and scarcity of computational resources and talent, developing countries are at disadvantage when it comes to employing data-driven, computational methods for studying the TH relations between universities, industries and governments. Scientometric analysis of the TH literature conducted in this study reveals a growing disparity between developed and developing countries in their use of innovative computational research methods. As a potential remedy, the extension of the TH model is proposed to include non-market actors as subjects of study as well as potential providers of computational resources, education and training. © 2013 Akadémiai Kiadó, Budapest, Hungary.
| Original language | English |
|---|---|
| Pages (from-to) | 175-186 |
| Journal | Scientometrics |
| Volume | 99 |
| Issue number | 1 |
| Online published | 23 Aug 2013 |
| DOIs | |
| Publication status | Published - Apr 2014 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
Research Keywords
- Big data
- Computational social science
- Developing countries
- Innovation
- Triple helix
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