# A Cognitive Model for Technology Adoption
## 2 Background and Related Work
[28] provided a model of technology adoption that considers both cognitive ability and receptiveness to advice as factors that influence the speed of adoption. One potential shortcoming of this research is that it focuses on a specific technology adoption case (GM corn seeds among US farmers), and it is unclear if the findings can be generalized to other technologies and contexts. Additionally, the study only considers cognitive ability and receptiveness to advice as factors influencing adoption and does not account for other important factors, such as social and economic factors. [PERSON] et al. [29] proposed to shift the focus from technological sophistication to the cognitive factors that affect blockchain adoption. One potential shortcoming of this research is that the sample may not be representative of the broader population of potential blockchain users. The study recruited participants from a specific geographic region or demographic group, which may limit the generalizability of the findings. [PERSON] et al. [30] identified six beliefs that affect the cognitive process of forming an attitude toward technology adoption. The study suggests that these factors play a crucial role in determining whether an individual will adopt new technology. The study only examines the impact of six beliefs on the formation of a behavioral intention to adopt, and there may be other important factors that were not included in the analysis. Additionally, the study only focuses on the formation of behavioral intentions and does not investigate actual adoption behavior or the subsequent diffusion process. [PERSON] et al. [31] introduced a new hybrid fuzzy multi-objective optimization algorithm to optimize the E-projects portfolio selection (EPPS) problem on social media, which involves choosing a set of investment projects that maximize return while minimizing risk. Figure 2: Internet (**a**) and Mobile phone (**b**) users adoption curves, two examples that support/validate the S-shape property of the technology adoption curve. This research integrates big data analytics and cognitive computing to provide a new model that can help resolve failures faced during big data analytics and improve decision-making processes in the industry. The study focuses on optimizing the EPPS problem on social media and does not explore other aspects of big data analytics and cognitive computing. Moreover, the study only focuses on the E-projects portfolio selection problem in a specific Iranian web development company, and it is unclear whether the proposed algorithm would work well in other contexts or industries. In another work, [PERSON] et al. [31] developed a system for early detection of elephants crossing forest borders, using a combination of multi-sensor data fusion, cognition theories, and computational intelligence techniques. This system provides a three-level detection of the target and its related outputs, along with interesting features about the target obtained through cognition theory. The study is focused only on the detection of a specific species, i.e., elephants in the Hosur forest region. Thus, the results may not be generalizable to other regions or animal species. A cognitive model for technology adoption may also be linked to data summarization because it allows for an understanding of how individuals process and make decisions about new technologies based on the information they receive. By summarizing data, it can provide a clear and concise representation of the information needed for individuals to make informed decisions about adoption, which can ultimately affect the success or failure of the technology in question [32; 33; 34]. It is also important to consider that a cognitive model for technology adoption could be linked to cognitive privacy because it can help understand how individuals perceive and make decisions about privacy risks when adopting new technologies.