The implications of big data for developing and transitional economies: Extending the Triple Helix?
- 946 Downloads
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.
KeywordsTriple helix Big data Computational social science Developing countries Innovation
This work was supported by the cluster funding scheme from the Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore. I would like to thank Zhu Qinfeng for her help with the database search and analysis.
- Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. New Haven, CT: Yale University Press.Google Scholar
- Black, A., Mascaro, C., Gallagher, M., & Goggins, S. (2012). Twitter zombie: Architecture for capturing, socially transforming and analyzing the Twittersphere. In Proceedings of the 17th ACM international conference on Supporting Group Work (GROUP '12) (pp. 229–238).Google Scholar
- Choi, S., Park, J., & Park, H. W. (2012). Using social media data to explore communication processes within South Korean online innovation communities. Scientometrics, 90(1), 43–56.Google Scholar
- Gurstein, M. B. (2011). Open data: Empowering the empowered or effective data use for everyone? First Monday, 16(2).Google Scholar
- International Monetary Fund. (2012). World Economic Outlook, October 2012. Retrieved from http://www.imf.org/external/pubs/ft/weo/2012/02/index.htm.
- Khan, G. (2012). Social media-based systems: An emerging area of information systems research and practice. Scientometrics, 95(1), 159–180.Google Scholar
- Manovich, L. (2012). Trending: The promises and the challenges of big social data. In: M. K. Gold (Ed.), Debates in the digital humanities. The University of Minnesota Press. Retrieved from http://lab.softwarestudies.com/2011/04/new-article-by-lev-manovich-trending.html.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.Google Scholar
- Petrovic, S., Osborne, M., & Lavrenko, V. (2010). Edinburgh Twitter corpus. In Proceedings of the workshop on social media (NAACL 2010).Google Scholar
- Yiu, C. (2011). The big data opportunity: Making government faster, smarter and more personal. Policy Exchange. http://policyexchange.org.uk/images/publications/the%20big%20data%20opportunity.pdf. Accessed 14 December 2012.
- Zhao, X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In Proceedings of the 33rd European conference on information retrieval (ECIR’11).Google Scholar