The study relied on analyzing the relationship between several factors that represent technological factors (Technology orientation, Utilizing new knowledge Technology Adoption, Knowledge compatibility, Technological capabilities and Technological collaboration) as independent factors and the level of performance as a dependent factor in medium and small-sized companies in the UAE economy. The inferential approach was followed in the study by analyzing the significance of the sampling results that were reached using structural equations based on the Partial Least Squares method (Pls) using the Smart Plus package, which was used to analyze the path between the six factors as independent factors and the performance factor as a dependent factor. The study applied this methodology to a sample of 250 individuals working in medium and small-sized companies in the UAE economy. The study concluded that the most closely related to performance was Technological Collaboration with an effect size of 72.3%. However, the least related factor was Technology orientation, with an effect factor of 34.4%, while the correlation of the other four factors with performance was medium (between 50% and 63%).

Smart farming is a feasible solution to help farmers effectively and sustainably manage the potential threats and risks those traditional farmers face, such as product quality, increased production costs, the environment, climate change, natural catastrophes, pests, and inferior goods. Using a survey research design, this research examined smart farming adoption and risk management models by combining the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT). The research sampled 400 farmers who are members of community enterprises in the northeastern region of Thailand. Data was collected using a questionnaire and analyzed using a statistical package program in four steps: confirmatory factor analysis, path analysis, structural equation model analysis (SEM), and Sobel's test. The findings revealed that government support variables had the most significant influence by adopting smart farming to risk management. Based on the research results, the government can apply this model to create strategies to encourage farmers to adopt smart farming and increase the production efficiency of agricultural products. The farmer can manage the risks of smart farming, which leads to sustainable smart farming and is useful for further academic acceptance and risk management studies. Furthermore, this study contributes to the existing literature on combining TAM and IDT in model adoption and risk management. The limitations include the small sample size adopted and the limited coverage area for the study, which restricts the generalization of the findings. However, the findings offer a glimpse into the benefits of smart farming.