Abstract
There is a strong interest in identifying and designing standardized indicators to measure and visualize the progress made by countries in terms of science, technology, and innovation with the purpose of defining strategies for the countries to increase their competitiveness. The main purpose of this study was to present a first approach to the identification of an alternative index of technological innovation based on the grouping of indicators reported by the Organization for Economic Cooperation and Development as inputs and outputs sub-indexes. The identified index made it possible to generate a ranking (A) of the positions of 37 countries and three groups of countries using longitudinal data analysis. The method used for validating the alternative technological innovation index was based on the calculation of Person’s correlations between our A ranking and the World Economic Forum ranking, the Global Competitiveness Index ranking, and the Subindex Innovation and Sophistication Factors ranking, in addition to the ranking presented in the Global Innovation Index by the World Intellectual Property Organization; high correlations validated our index and ranking.
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Notes
The intensity of the relationship (correlation) can indeed change over time or when the number observations increases. Accordingly, the value of such intensity was not considered in the present study, only the sign of this intensity.
The use of panel data models to obtain empirical forecasts has been observed to increase (Greenaway-McGrevy 2015). Undoubtedly, one of the main advantages of using panel data is that they show the individual and temporary effects of each variable (Wu and Li 2014). Nevertheless, it is also true that most literature on panel data is focused on econometric models, which are used to make predictions (Baltagi et al. 2012). Unlike those studies, panel data were not used in the present study as an input for an econometric model, but to determine the direct relationships among the study variables, which were treated longitudinally. The intensity of such relationships, determined by panel data, allowed for the construction of small clusters (Trindade et al. 2017).
These indicators were classified with the purpose of constructing two main pillars (input and output indexes) concerning science and technology in each OECD member country. This classification was intended as a first quantitative approach to the cost–benefit of individual OECD member countries during the study period in terms of their efforts to create, implement, and develop top-notch science and technology.
It is considered a positive linear correlation (or first-order correlation) because its purpose is to merge a set of variables whose causalities share the same sign (in this case, a positive correlation). In other words, the present analysis assumes that variable relationships are linear; the investigation also assumes that each factor carries all the information needed for its measurement and is not partially affected by one or several other factors.
As has been indicated, the intensity of the relationships among variables (calculated correlation) was not the factor used for their discrimination. Instead, the sign of such intensity was considered in the selection of the study variables.
Pearson’s correlation coefficient served to verify the causal relationship between the involved variables. In this case, it can be argued that, whereas a set of variables grows (positive correlation), a study variable decreases (negative correlation); however, this negative intensity (correlation) provides no additional information of the same sign to the set of variables having a positive causal relationship. In other words, the present study sought that each variable considered in the indicator that was then used to build the ranking added to the positive relationship with the other selected variables. For example, when the growth of the variable termed ‘GERD as a percentage of GDP’ is presented, these data are expected to have a positive impact on the variable termed ‘number of patents in biotechnology sector applications filed on the PCT (priority year)’.
The adjusted average was considered because it reflects a greater representativity of the behavior of the analyzed data during the study period than the real average. If the real average was considered, then information regarding the years in which there were no data would be lost, and therefore, it would decrease the scores of countries that reported few results. The use of the adjusted average during the 2000-2013 compensates for the fact that only the years for which there were data were considered. In other words, the denominator for the calculation of this average will not be greater than 14 (total years of the study period).
In general, multicollinearity addresses the linear relationship between exogenous variables included in the regression. The concept of collinearity was limited when the common practice was to include only two types of exogenous variables (Greene 2011).
Data used for the definition of the study period were also selected based on availability and continuity of the latest data present by the OECD (2016op. cit.).
Although more recent versions of the Global Competitiveness Report and the Global Innovation Index available, the 2012–2013 and 2013 versions were considered so that the comparison with the data reported by the OECD for 2013 was more homogeneous.
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We would like to acknowledge the support provided by the National Polytechnic Institute (Instituto Politécnico Nacional) and the Secretariat for Research and Postgraduate Studies (Secretaría de Investigación y Posgrado), Grants number 20171165, 20180919 and 20180205. We would also like to thank CONACYT for the postdoctoral grant awarded to Dr. Gerardo Reyes Ruiz.
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Barragán-Ocaña, A., Reyes-Ruiz, G., Olmos-Peña, S. et al. Approach to the identification of an alternative technological innovation index. Scientometrics 122, 23–45 (2020). https://doi.org/10.1007/s11192-019-03292-9
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DOI: https://doi.org/10.1007/s11192-019-03292-9