# A Cognitive Model for Technology Adoption
## 2 Background and Related Work  
A cognitive model can help to understand how individuals perceive and evaluate the risks and benefits of new technology, and how this influences their decision to adopt or not. This can inform the design of privacy-enhancing features, making it more likely that individuals will adopt the technology, and also help to identify potential biases or misconceptions that individuals may have about privacy, which can be addressed to increase trust and adoption. For example, a cognitive model can help to understand how individuals process recommendations from their social network, which can inform the design of recommender systems to better align with those processes, making it more likely that individuals will adopt the technologies being recommended. One use case that highlights the need for a cognitive model for technology adoption is the adoption of new medical technologies by healthcare professionals. Medical technologies such as electronic health records, telemedicine, and remote patient monitoring are rapidly emerging in the healthcare industry. However, the adoption of these technologies has been slow and uneven across healthcare providers, with some embracing the new tools while others resist their implementation. A Cognitive Model for Technology Adoption can help to explain why some healthcare professionals are more willing to adopt these new technologies than others. For example, the model can explore how cognitive factors such as perception, beliefs, attitudes, and decision-making influence the adoption process. Additionally, the model can examine how individual differences in cognitive abilities, such as cognitive load and mental workload, impact technology adoption. By understanding the cognitive factors that influence technology adoption in the healthcare industry, we can develop interventions and strategies that facilitate the adoption of new medical technologies. This can help to improve patient care and outcomes, reduce healthcare costs, and enhance the overall efficiency of the healthcare system. Much research has been conducted to explore the behavior of technology adoption by firms. In one of the models known as the Technology Acceptance Model (TAM), 'Perceived Usefulness' and 'Perceived Ease of Use' of the technology are considered the main factors in technology adoption. These two variables were found to consistently explain 40% of the variance in an individual's intention to use and implement a technology [14]. In [PERSON]'s Innovation Diffusion Theory (IDT) [11], the most well-known of the models, the focus is on how new technology will be adopted by members of a population through communication by human-interpersonal networks. Based on this model, the demand for innovation is a function of time, and the cumulative form of innovation diffusion among the population versus time follows an S-shaped curve, with an early minority leading to a tipping point of mass take-up, followed by a plateau. Using [PERSON]'s terminology, the technology adopters fall into five different categories named Innovators (2.5%), Early adopters (13.5%), which lead the trend, followed by Early majority (34%), then Late majority (34%), and, finally, Laggards (16%) [11]. At the organizational level, the \"Technology, Organisation and Environment\" (TOE) framework developed by [PERSON] et al. in 1990 [15] has identified the three following constructs that influence the adoption of technology by an organization: (i) The internal and external technological context, such as availability, best practices, and equipment; (ii) The organizational context such as firm size, slack, communication processes, and managerial structure; and (iii) The environmental context of industry characteristics, market characteristics, technology support structures, and government regulations.