# A Cognitive Model for Technology Adoption
## 3 Methodology  
**Emerging Technology Adoption S-Curve, (ETASC). ** Our technology adoption model, referred to as Emerging Technology Adoption S-Curve (ETASC), is an extension of all the above prior models (see Section 2) and is used to determine the adoption of technology specific to a country, industry, and organization. A typical technology adoption S-Curve with all its influencing factors is shown in Figure 3. The tipping point of the curve corresponds to the time when the costs are reduced over time, and the value would be realized. The output of the adoption model will help enterprises in making optimal decisions and policies and also in market planning [35]. The methodology for predicting the impact of emerging technologies using the ETASC model can be formulated using the framework illustrated in Figure 4. The framework consists of four stages:  
* Data Curation: In the first stage, the ETASC analysis is performed to gather performance indicators for each country from multiple data sources, which include patent databases, scientific publications, and technological news sources. The data is then pre-processed to ensure consistency and completeness, after which missing values are imputed using statistical techniques such as mean imputation or regression imputation. * Feature Engineering: In this stage, a novel method is used to extract and weigh the countries' influencing features. This involves identifying the key factors that impact technology adoption, such as economic development, regulatory environment, cultural norms, and infrastructure. The features are then assigned weights based on their relative importance in predicting adoption. * Clustering: In this stage, a machine learning-based algorithm is used to group countries based on their weighted features and rank them. This involves selecting an appropriate clustering method, such as k-means or hierarchical clustering, and determining the optimal number of clusters using techniques such as the elbow method or silhouette analysis. The resulting clusters are then ranked based on their adoption potential. * Adoption Modelling: in the fourth stage, the ETASC model combines the country rankings with the industry adoption data to estimate the impact of the emerging technology for each Country-Industry pair and companies. The model uses a novel parametric mathematical adoption model enriched by technology domain knowledge. This framework provides a structured and systematic approach for predicting the impact of emerging technologies, ensuring a high level of accuracy and reliability in the prediction results. **ETASC Analysis. ** The ETASC model requires an adoption curve describing the fastest possible technology adoption by a country and industry. This 'base' curve is then adjusted to the likely adoption of the same technology by other countries and industries based on the collected global indicators. The initial base curve is determined through bottom-up research like the one for RPA in Section 4. We have evaluated each country's technology acceptance and readiness on five different pillars. These pillars on our ETASC analysis platform are grouped as: Professionals and human resources, Market and competition, Finance and Economy, Infrastructure, and Societal attributes. Each pillar of our ETASC analysis could be divided into some subcomponents or productivity drivers, which are the  
Figure 4: A snapshot of our Data-Driven pipeline for Technology adoption curve prediction with five stages, including the Machine Learning module and a novel parametric mathematical model stages. Figure 3: Technology Adoption S-Curve and influencing factors. basis of the countries' assessment and scoring. This grouping schema and some attributes of each group are presented in Table 2.  
Countries are assessed annually on their productivity drivers by international organizations such as World Bank, World Economic Forum (WEF), UNESCO, and even Central Intelligence Agency (CIA).