# A Cognitive Model for Technology Adoption
## 3 Methodology
This value was determined through an iterative process of evaluating the within-cluster sum of squares (WCSS) and silhouette score for different values of K. Once the clustering was performed, a score was calculated for each country in the clusters by summing the weighted features. This score represents the overall technology adoption capability of the country based on the selected attributes. Finally, the countries in each cluster were ranked based on this final score. It is important to note that the selection of the attributes and the choice of the machine learning algorithm can significantly impact the results of the clustering analysis. Therefore, it is essential to carefully evaluate the performance of different algorithms and attribute selection techniques to ensure that the resulting clusters are meaningful and reliable. **Sectoral Dependency. ** Adopting emerging technology not only depends on the country and its features, but also on the maturity of the different sectors. Recent research from McKinsey Global Institute (MGI) has found a large and growing gap between various industries in the U.S. economy [36]. To the best of the author's knowledge, the most comprehensive research on the digital intensity of the sectors has been carried out by OECD (Organization for Economic Cooperation and Development), which covers 36 ISIC Rev.4 sectors over the period 2001-2015 in its member countries [37]. Due to the multidimensionality of the collected information from a wide spectrum of industries and countries, this research is called a global sector taxonomy. The digital intensity of the sectors has been studied from three main aspects: Technological components, Human capital, and how digital technology would change the sector's impact on the economy. Then the indicators used in the classification of the industries' digital intensity in OECD global taxonomy were selected as the share of ICT tangible and intangible (e.g., software) investment, the share of purchases of intermediate ICT goods and services, the stack of robots per hundreds of employees, the share of ICT specialists in total employment, and the share of turnover from online sales in each industry. These attributes would help us better understand the digital transformation's sectoral heterogeneity. Hence, we decided to use this global sector taxonomy results in classifying the technology adoption of each industry in our method. The list of some industries and their technology adoption rates resulting from the global sector taxonomy is presented in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Industry** & **Adoption Rate** \\ \hline
1 & Information and communication & Fast \\
2 & Financial and insurance activities & Fast \\
3 & Professional, scientific and technical activities & Fast \\
4 & Administrative and support service activities & Fast \\
5 & Manufacturing & Fast \\
6 & Wholesale and retail trade; repair of motor vehicles and motorcycles & Fast \\
7 & Public administration and defence; compulsory social security & Average \\
8 & Education & Average \\
9 & Human health and social work activities & Average \\
10 & Arts, entertainment and recreation & Average \\
11 & Other service activities & Average \\
12 & Transportation and storage & Average \\
13 & Accommodation and food service activities & Average \\
14 & Mining and quarrying & Average \\
15 & Agriculture, forestry and fishing & Slow \\
16 & Electricity, gas, steam, and air conditioning supply & Slow \\
17 & Water supply; sewage, waste management and remediation activities & Slow \\
18 & Construction & Slow \\
19 & Real estate activities & Slow \\
20 & Activities of households as employers & Slow \\
21 & Activities of extraterritorial organizations and bodies & Slow \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of industries and their adoption rate.