Impact of COVID-19 on Online Food Channels and Consumer Behavior in Saudi Arabia

Following the rapid spread of coronavirus 'COVID-19' around the world and the imposed confinement measures by governments, the food retailing sector has experienced tremendous shifts in trends among consumers. Due to uncertainty of the future, the food sector faced an imbalance in supply demand, following the shift towards online grocery shopping to mitigate the risk of infection from supermarkets. Towards the end of the first lockdown restrictions imposed by the Saudi Arabian government, this research studies the impact of COVID-19, a disease created by corona virus, on the intention to continuously use online food channels post pandemic. In this research, we intend to evaluate the consumer factors that determine their intention to use online food channels even after the confinement measures. The study focuses on the interaction between the independent variables, customer satisfaction, social influence, trust and convenience and the dependent variable intention to use online food channels. The study was conducted on 528 Saudi citizens using a questionnaire distributed through social media. The questionnaire consisted of demographic information questions and questions related to each of the independent variables, which were analyzed using SPSS-22 software. The findings revealed that there is a significant association between the consumer factors and the intention to use online food channels post-pandemic. © 2022 Bharati Vidyapeeth, New Delhi.

A novel technique for early detection of COVID-19

COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease. Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages. However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible. Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images. This paper proposes an automatic technique to classifyCOVID-19 patients from their computerized tomography (CT) scan images. The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data. We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature. Also, we conduct principal component analysis, which is used for dimensional deduction. Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient. © 2021 Tech Science Press. All rights reserved.