# A Cognitive Model for Technology Adoption
## 1 Introduction  
Understanding how individuals make decisions about technology adoption is crucial for designing technologies that maximize the chances of a successful adoption. A cognitive model that can explain the complex decision-making process that individuals undergo when evaluating and deciding to adopt new technologies is particularly valuable. Despite the importance of this, the existing approaches to understanding technology adoption fall short of adequately addressing the cognitive processes involved. A well-designed cognitive model can inform the design of new technologies and the way they are presented to users to increase the likelihood of adoption. Moreover, by identifying potential barriers to adoption, such as misconceptions and biases, cognitive models can aid in addressing these issues and ultimately enhance the success of the technology [1, 2, 3, 4]. For example, cognitive models can shed light on how individuals process and make sense of new information about a technology, how it is integrated with their prior knowledge and experiences, and how it ultimately influences their decision-making. The rapid growth in advanced technologies such as Artificial Intelligence (AI), Machine Learning, and Robotics is transforming the nature of work in every industry globally [5]. The impact of this fourth industrial revolution, or Industry 4.0, is significant, leading to the automation or augmentation of work tasks that once required human skills and activities [6]. However, the current approach to technology adoption falls short in addressing the skills shock that can arise from this transformation, where workers are not up-skilled or re-skilled in time to perform new tasks and jobs [7]. To prepare for the future impact of Industry 4.0 on job markets, policymakers and firms need better models for future scenario planning. A deeper understanding of technology adoption will enable governments, firms, and individuals to better transition to a new future of work [8]. For instance, identifying when Robotic Process Automation could automate many back-office functions within an organization can help Human Resource teams re-skill their employees into new careers with better prospects. Despite the importance of technology adoption models, the current approach is limited in addressing the skills shock and the overall impact of Industry 4.0 [7; 8]. There is a lack of comprehensive, global models that can account for the diversity of locations, industries, and organizations. This shortcoming highlights the need for better models that can help policymakers and firms to prepare for the future impact of Industry 4.0 on job markets, and ultimately, to better manage the transition toward a new future of work. The existing literature on technology adoption has discussed various theoretical perspectives and empirical findings related to the need for a cognitive model for technology adoption [2; 9; 10]. For example, the Diffusion of Innovation (DOI) theory [11]--which explains how new ideas, products, or technologies spread through a population or social system--highlights that the technology adoption is influenced by individual perceptions of the innovation's relative advantage, compatibility, complexity, triablility, and observability. As another example, the Technology Acceptance Model (TAM) [1]--which focuses on the user's perceptions of the technology's usefulness and ease of use--highlights that the user's attitude towards the technology and its perceived usefulness and ease of use determine their intention to use the technology. However, these existing approaches have some shortcomings. For example, the DOI theory does not account for individual differences in perceptions and decision-making processes, and the TAM does not consider the social and organizational factors that influence technology adoption.