Abstract
The rise of emerging technologies is a priority for generating innovative products and processes aimed at contributing to the solution of cutting edge problems for smart cities and villages. The progress and development of nanotechnology make possible to market leading technological applications in fields as diverse as medical science, new materials development, and electronics, among others. The present study has two main aims. First, to determine whether the (average) production of IP5 nanotechnology patent families from 1999 to 2013 had a first-order relationship with the applicant’s place of residence (priority date) and inventor’s place of residence (priority date) during the same period in The Organisation for Economic Co-operation and Development (OECD) (The OECD is an international organism whose main mission is generating policies focused on the advancement and well-being of the countries that integrate it, promoting actions oriented toward the attention of environmental, economic and social problems (OECD in Who we are, 2019 [34].) member countries, and second, to determine whether the formation of this type of patent families by place of residence of applicant (priority date) in 2013 is associated with five variables. Results show that a relationship exists between variables in both cases, especially in the second, in which the weight of the participation of research scientists and the industry’s value-added is quite significant. This approach allows for conclusions concerning OECD member countries and also has implications for the development of nanotechnology in Latin America to consolidate as smart cities and villages.
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Notes
- 1.
Spearman’s correlation coefficient is a non-parametric statistic tool but it lacks a probabilistic distribution which can be established as an association measure between two variables. This statistic describes the monotonic relationship between variables and it is used when data distribution is unreliable through the Pearson’s linear correlation coefficient. To calculate Spearman’s statistic is required that variables must be arranged in an ordinal scale so that each observation can be assigned a rank and placed in its corresponding series in an orderly fashion [15, 41].
- 2.
Spearman’s correlation coefficient cannot be considered a first-order association measure between two variables. Nevertheless, when assumptions about the frequency distributions of the study variables are unavailable, Spearman’s r correlation coefficient can be used to show the setting of an arbitrary monotone function to be used for describing the relationship between variables. Moreover, unlike Pearson’s correlation coefficient, Spearman’s coefficient can be used without the assumption that the relationship between the variables is of first-order, and variables need not to be expressed using an interval scale. Spearman’s coefficient can thus be calculated using an ordinal scale for the variables (see [15]. Therefore, Spearman’s correlation coefficient is an excellent non-parametric alternative to Pearson’s correlation coefficient when the latter fails to meet the normality assumption.
- 3.
An example of direct factor may well be the economic investment that a country or region allocates to nanotechnology-related research, whereas the number of nanotechnology-related patents is an example of indirect factor.
- 4.
This arithmetic mean, or first moment, is calculated from direct quantitative data, and it can be converted into positions or ranks.
- 5.
To carry out hypothesis test H0: \( \rho_{S} = 0 \) versus H1: \( \rho_{S} \ne 0 \) uses statistic \( t^{*} = \frac{{r_{s} }}{{\sqrt {\frac{{1 - r_{s} }}{n - 2}} }} \) which presents a Student’s t distribution with n − 2 degrees of freedom (where n represents number of observations). The decision rule to contrast this hypothesis is: Do not reject \( {\text{H}}_{0} : \rho_{S} = 0 \) when \( \left| {{\text{t }}^{ *} } \right| > {\text{t}}_{{{\text{n}} - 2}}^{\alpha }. \)
- 6.
When two or more values are equal, the rank for each one will be the average of the ranks that would correspond to such values if they were different.
- 7.
Even though the concept of collinearity refers to a single perfect linear relationship between two explanatory variables, the term multicollinearity refers to more than one relationship of this kind between independent variables. Therefore, the term ‘multicollinearity’ is used in a more generic sense, with the purpose of including both cases (see [13, 14]).
- 8.
Jarque-Bera statistic is used for testing data normality. The null hypothesis H0 assumes data normality through asymmetry coefficient (m3) and kurtosis coefficient (m4). The test statistic is \( JB = n*\left[ {\frac{{m_{3}^{2} }}{6} + \left( {\frac{{m_{4} - 3}}{24}} \right)^{2} } \right] \), which presents a Chi-squared distribution with two degrees of freedom.
- 9.
The lack of data normality does not limit the significance of Spearman’s correlation coefficient; this statistic verifies that prob (\( r_{s} \ge k | H_{0} )= \alpha \). Nevertheless, as a consequence of this result, coefficient rs cannot be considered for establishing a codependence (causality) of first-order between variables X and Y.
- 10.
In tables, this value corresponded to a Student’s t with 30 degrees of freedom and two-tailed α = 0.05.
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Acknowledgements
The authors acknowledge the financial support provided by the National Polytechnic Institute (Instituto Politécnico Nacional), Secretariat for Research and Postgraduate Studies (Secretaría de Investigación y Posgrado), grant numbers 20180919 and 20180067. Dr. Gerardo Reyes thanks the support provided by the National Polytechnic Institute (Centro de Investigaciones Económicas, Administrativas y Sociales-CIECAS) through the program of national postdoctoral research stays coordinated by CONACYT (National Council for Science and Technology).
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Barragán-Ocaña, A., Reyes-Ruiz, G., Merritt, H. (2020). Scientific, Technological, and Innovation Dynamics in Nanotechnology for Smart Cities and Villages: The OECD Case and Its Implications for Latin America. In: Patnaik, S., Sen, S., Mahmoud, M. (eds) Smart Village Technology. Modeling and Optimization in Science and Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-37794-6_3
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