# A Cognitive Model for Technology Adoption
## 1 Introduction
Additionally, these approaches focus primarily on the adoption of individual technologies and do not provide a comprehensive framework for understanding the adoption of multiple technologies and their interactions within an organizational context. Therefore, there is a need for a cognitive model for technology adoption that integrates individual, social, and organizational factors and considers the adoption of multiple technologies within an organizational context. This model should account for individual differences in cognitive processes and decision-making and provide a more comprehensive understanding of technology adoption in organizations [12; 13]. This is important as in the era of Industry 4.0, the adoption of new products can take place through various recommendation pipelines. However, the adoption of new technologies by firms depends on a range of factors, and several firm-level technology adoption models exist in both scientific and management literature [8]. Despite this, technology adoption does not happen simultaneously across all firms, but rather is a process where some industries or countries are more willing and capable of adopting new technologies than others. Another shortcoming of the existing approach is that while it explains the process of technology adoption, it does not fully address the reasons for differences in the willingness and ability of firms and industries to adopt new technologies. This highlights the need for further research to develop a more comprehensive understanding of the factors that influence technology adoption by firms. To address these challenges, in this paper, we introduce a new approach to enhance the prediction of future impacts of emerging technology on job markets. The proposed approach offers several unique contributions that are significant improvements compared to the existing state of the art:  
* We first present a novel data-driven pipeline and machine learning algorithms to model the extent and rate of technology adoption for each country and industry. This contributes to a more accurate and precise prediction of technology adoption rates, which can help policymakers, firms, and researchers better plan for future technological changes. * Our proposed approach derives global indicators as features to determine technology adoption curves for a country and industry. This novel approach provides valuable insights into a firm's maturity on the adoption curve, which can help firms better plan for technological changes. * We apply global indicators to validate the prediction model and provides a detailed analysis of country and industry technology adoption rates. This contributes to the development of a more comprehensive understanding of the factors that influence technology adoption by firms and provides insights into the differences in the willingness and ability of firms and industries to adopt new technologies. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning. By predicting the adoption rate of emerging technologies, organizations can better prepare for the future, make informed decisions about technology investments, and develop appropriate strategies to ensure the skills and competencies of the workforce align with emerging technology requirements. The rest of the paper is organized as follows: We present the background and the related work in Section 2. In Section 3, we introduce our data-driven pipeline for technology adoption prediction with a detailed explanation for all the stages. We propose a novel mathematical model for predicting the adoption of emerging technologies in Section 4 along with the obtained results. Finally, we conclude the paper in Section 5.