# A Cognitive Model for Technology Adoption
## 3 Methodology
Among these, WEF annual reports and datasets are the most comprehensive and up-to-date, including twelve productivity drivers or pillars of the countries. The twelve pillars of WEF have more than one hundred indicators, sourced from international organizations, academic institutions, and non-governmental organizations. The values of 169 indicators for 130 countries in 2017-2019, collected from WEF, CIA factbook, Worldbank, and UNESCO, are used as our main data source. These indicators are used as the countries' attributes in our Machine Learning based model. This schema and associated attributes reflect the motivation and ability of a country's public and private sectors to adopt new and emerging technologies. **Feature Selection and Weighting. ** In the feature engineering phase, the goal was to identify the most relevant and non-redundant attributes for the technology adoption topic. This involved a two-step process of data cleaning and feature selection. In order to select the optimal features for this approach, a two-step process of data cleaning and feature selection was performed. The data cleaning phase involved imputing missing values using regression models and normalizing and standard scaling to ensure all attributes were on the same scale. The feature selection step was performed using correlation analysis, with a focus on identifying attributes that were highly correlated with the Mobile broadband subscription per 100 population attribute. This step ensured that only attributes that were strongly correlated with technology adoption were retained for further analysis. The use of multiple regression models for imputing missing values and correlation analysis for feature selection ensures that the dataset is accurate, relevant, and useful for subsequent analysis and modeling techniques. Feature selection is a critical step in the data analysis process as it reduces the dimensionality of the dataset and improves the accuracy and efficiency of the subsequent analysis or modeling techniques. Having collected all available country data, the data required cleaning. In this data cleaning phase, some data points were imputed by projecting their corresponding values from prior years, if available, otherwise predicted by the best-fit regression model. Three regression models, including linear, random forest, and XGBoost, were fitted, and the best-fit model was selected for each missing attribute. Normalizing and standard scaling was applied to this clean dataset before doing any more analysis or processing. Among those 169 normalized attributes, some could be redundant, and another part could be less relevant to the technology adoption topic. Feature selection for eliminating redundant or non-relevant attributes is made by running the correlation analysis \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Attribute** & **Group** \\ \hline
1 & Digital skills among active population & Professionals and Human resources \\
2 & Technicians and associate professionals & Professionals and Human resources \\
3 & Senior officials and managers & Professionals and Human resources \\
4 & Extent of market dominance & Market and Competition \\
5 & Competition in professional services & Market and Competition \\
6 & Collaboration between companies & Market and Competition \\
7 & R\&D expenditures & Financial and economy \\
8 & Domestic credit to private sector & Financial and economy \\
9 & Financing of SMEs & Financial and economy \\
10 & E-Participation & Infrastructure \\
11 & Mobile-broadband subscriptions & Infrastructure \\
12 & Fibe internet subscriptions & Infrastructure \\
13 & societal capital & societal \\
14 & Efficiency of the legal framework in challenging regulations & societal \\
15 & Conflict of interest regulation & societal \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some indicators of the countries and their grouping. between each pair of attributes, including the countries' Mobile broadband subscription per 100 population attribute.