# A Cognitive Model for Technology Adoption
## 4 Technology Adoption Curve Prediction  
**Base Adoption Curve** To seed the ETASC model, we first model the fastest possible S-curve for emerging technology. Here we develop a curve for Robotic Process Automation (RPA). RPA is software commonly known as a \"robot\" or bot. RPA technology can perform rule-based processes. Looking at Google trends, VC fundings, start-ups, and patents statistics on RPA since 2010 till now and also Deloitte's global RPA survey [38], has identified that the technology was commercially available in 2017, as shown in Figure 5. RPA is a cost-saving technology, and it's estimated that its adoption will be over 90%. Based on our research and study of the trends, we estimate that 90% of organizations in a fast-adopting industry will have completed a pilot project for RPA technologies by 2024, and the mass-scaled adoption will be reached in 2028 in a fast-adopting country like Singapore or the United States as shown in Figure 6.  
**Mathematical Adoption Model** The parametric hyperbolic trigonometric function is a mathematical equation proposed in Equation (3) to model the S-shaped adoption curves in the Bass model, which is a widely used model to describe the diffusion of new products or technologies in a population. The equation takes into account four parameters: \(k1\), \(k2\)  
Figure 5: The trends of RPA technology’s attributes, including Google trend, VC Funding, Start-ups, and Patents trends in the past decade. Figure 6: Fastest RPA adoption by firms in a fast adopting industry/country. \(k3\), and \(k4\), where the likelihood of adoption is a function of time (t) and these parameters. Specifically, the equation is of the following form:  
\[\textit{Likelihood of Adoption}=k1+k2*tanh(k3*(t-k4)/100), \tag{3}\]  
where \(tanh\) is the hyperbolic tangent function. To apply this equation to specific countries and industries, we use the ranking scores obtained in the previous section to interpolate the k parameters for each (Country, Industry) pair. The ranking scores are based on factors such as the country's level of economic development, technological infrastructure, and social and cultural characteristics, as well as the industry's level of competitiveness, market size, and regulation. Once we have these interpolated k parameters, we can predict the likelihood of technology adoption over a time interval of 20 years using the resulting S-shaped curve. This allows us to estimate how quickly or slowly a particular technology will diffuse in a given country and industry, taking into account its specific characteristics. As an example, we applied our model to RPA (Robotic Process Automation) technology adoption in the finance and insurance industry in three sample countries: Singapore, Indonesia, and Zimbabwe. These countries were chosen to represent fast, average, and slow adopters within their respective clusters. The resulting k parameters for each country and industry pair are listed in Table 5, and the corresponding adoption curves are shown in Figure 7. These curves illustrate the predicted likelihood of adoption over time for each country and industry, based on their unique characteristics and the parameters of the hyperbolic trigonometric function. DiscussionThe proposed model would facilitate the prediction of emerging technologies' impact on each country and industry. This novel method can help public and private sector policymakers to make timely and accurate decisions about the future job market. In this way, organizations could control the unemployment rate and minimize the related costs. Governments and business owners would implement this control in various forms, such as reshaping their organizational structures, cultures, and processes to fit these changing developments. The up-skilling program for the current employees also would prepare them for the inevitable job transitions. All these insightful results have been obtained by leveraging AI and ML technologies on the available data on the job market and emerging technologies.