Estimation of the Country Ranking Scores on the Global Innovation Index 2016 Using the Artificial Neural Network Method
İhsan Pençe,
Adnan Kalkan () and
Melike Şişeci Çeşmeli ()
Additional contact information
İhsan Pençe: Bucak Zeliha Tolunay Applied Technology and Business School, Mehmet Akif Ersoy University, Adem Tolunay Campus, BURDUR, Bucak, Turkey
Adnan Kalkan: Bucak Zeliha Tolunay Applied Technology and Business School, Mehmet Akif Ersoy University, Adem Tolunay Campus, BURDUR, Bucak, Turkey
Melike Şişeci Çeşmeli: Bucak Zeliha Tolunay Applied Technology and Business School, Mehmet Akif Ersoy University, Adem Tolunay Campus, BURDUR, Bucak, Turkey
International Journal of Innovation and Technology Management (IJITM), 2019, vol. 16, issue 04, 1-16
Abstract:
The Global Innovation Index (GII) aims to rank countries using different innovation factors. This ranking list enables countries to observe their potential status according to the rankings of other countries. The countries are classified under four groups according to the World Bank Income Group Classification on the GII list. The groups are named as; low income (LI), lower-middle income (LM), upper-middle income (UM) and high income (HI). Also, every country has a score in this ranking list. In this study, the ranking scores of 128 countries are estimated using the artificial neural network (ANN). We chose the relevant 27 features on GII 2016 Report, as input data. The significance of this paper is that; it is the first curve fitting and estimation of the score processes on GII 2016 dataset. The low root mean square error (RMSE) value which is obtained in an experimental study shows that the fitting structure is good enough to determine the approximate score of the countries in GII list. The results also show that the selected 27 features are sufficient for obtaining the income score of the countries. Increasing the number of features would lower the RMSE value and enable better approximation in the curve fitting process. The final results can assist the countries in achieving long-term output growth and improving their innovation capabilities.
Keywords: Global innovation index; information and communication technologies; curve fitting; artificial neural network (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219877019400078
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitmx:v:16:y:2019:i:04:n:s0219877019400078
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219877019400078
Access Statistics for this article
International Journal of Innovation and Technology Management (IJITM) is currently edited by H K Tang
More articles in International Journal of Innovation and Technology Management (IJITM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().