Consumers' acceptance of social commerce during COVID-19 lockdown

The COVID-19 pandemic changed everything, especially marketing, leading to increased digital usage. Social media allows faster connectivity among people and gives marketers new pathways to engage with consumers. The lockdown dramatically reduced economic activity by numbers that are worth understanding. This study examines the numerous aspects contributing to the consumer's favorable opinions toward their social commerce intents and behavior during the COVID-19 pandemic. Using SEM, the data examination of 297 respondents established that applying the social support theory and the unified theory of acceptance and use of technology (UTAUT) model to the proposed theoretical framework is significantly associated with social commerce intentions. The results state that all the direct hypotheses have been supported, confirming that social support, performance expectancy, effort expectancy, offline subjective norms, and online subjective norms are significantly associated with social commerce intentions. The results also indicated that Information Technology Infrastructure (ITI) moderated social support, performance expectancy, effort expectancy, and online subjective norms. © 2022 Korean Scholars of Marketing Science.

Consumers' acceptance of social commerce during COVID-19 lockdown

The COVID-19 pandemic changed everything, especially marketing, leading to increased digital usage. Social media allows faster connectivity among people and gives marketers new pathways to engage with consumers. The lockdown dramatically reduced economic activity by numbers that are worth understanding. This study examines the numerous aspects contributing to the consumer's favorable opinions toward their social commerce intents and behavior during the COVID-19 pandemic. Using SEM, the data examination of 297 respondents established that applying the social support theory and the unified theory of acceptance and use of technology (UTAUT) model to the proposed theoretical framework is significantly associated with social commerce intentions. The results state that all the direct hypotheses have been supported, confirming that social support, performance expectancy, effort expectancy, offline subjective norms, and online subjective norms are significantly associated with social commerce intentions. The results also indicated that Information Technology Infrastructure (ITI) moderated social support, performance expectancy, effort expectancy, and online subjective norms.

Selecting a Better Classifier Using Machine Learning for COVID-19

This paper will elaborate that how timely available data and Machine learning algorithms can help in determining premature exposure of coronavirus (COVID-19) and aided the world in formulating to reduce the loss. We will investigate which machine learning algorithms are best fit to predict COVID-19 data sets. In this study our focus will be on the spread of COVID-19 internationally in different countries. This study will serve as a resource for the future research and development on COVID-19 by producing better research in this field. To achieve the outcomes and future forecasting of COVID-19, we analyze the records and datasets of COVID-19 through Machine Learning algorithms. For this purpose, we used six algorithms to construct classifiers such as K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM), Naive Bayes, Logistic Regression and Random Forecast. These algorithms were applied on Python a machine learning software. The dataset is acquired by WHO data sets and data sets provided online at GitHub and compiled and organized by different communities to track the spread of the virus. The Performance of the best classifier will be measured using Accuracy. The model developed with Decision Tree is one of the most efficient classifier with the highest percentage of accuracy of 99.85 percent, and is followed by Random Forecast with 99.60 percent, Naive Bayes with 97.52 percent accuracy, Logistic Regression with 97.49 percent accuracy, Support Vector Machine with 98.85 percent accuracy and K-NN with 98.06 percent accuracy. In our research, we discussed two types of classification: Binary and Multinomial. Support Vector Machine and Decision Tree give us precise results. Other classifier models gave satisfactory outcomes. The outcomes may be helping to predict the future circumstances of COVID-19. From the past studies we have used Autoregressive integrated moving average (ARIMA) model for time series data. SIR models to check the spread of Nowcasting and forecasting the spread of 2019-nCoV in China and worldwide. © 2022 University of Bahrain. All rights reserved.

A cross sectional survey on the effect of COVID-19 related restrictions on undergraduate and postgraduate medical education in Qatar.

BACKGROUND: COVID-19 pandemic has affected all dimensions of day to day life across the world and medical education was no exception. With this study, we aimed to understand the effect of nationwide restrictions on medical education in Qatar, the models of education adopted during this period and perceptions of participants to the same. METHODS: We conducted a cross-sectional study utilizing an online questionnaire distributed via convenience sampling between April-October 2020. Study participants were faculty and trainees in governmental undergraduate and postgraduate medical education institutes. Two sets of questionnaires were designed for each group. They were asked a series of questions to assess pre- and post-COVID pandemic educational practices, their preferred teaching methods, and their familiarity with electronic teaching platforms. Faculty respondents were asked about their perceived barriers to delivery of medical education during the pandemic and their agreement on a 5-point Likert scale on specific elements. Trainees were asked a series of multiple-choice questions to characterize their pre- and post-COVID pandemic educational experiences. Both groups were asked open-ended questions to provide qualitative insights into their answers. Data were analysed using STATA software version 12.0. RESULTS: Majority of trainees (58.5%) responded that the pandemic has adversely affected medical education at both the undergraduate and postgraduate levels. Trainees (58.5%) and faculty (35.7%) reported an increased reliance on e-learning. Trainees preferred face-to-face education, while faculty preferred a combination of models of education delivery (33.5% versus 37.1%, p = 0.38). Although 52.5% of the faculty had no previous experience of delivering education through e-learning modalities, 58.9% however felt confident in using e-learning software. CONCLUSIONS: Faculty and trainees agree that the COVID-19 pandemic has had a significant impact on the provision of medical education and training in Qatar, with an increased dependence on e-learning. As trainee's prefer face-to-face models of education, we may have to consider restructuring of medical curricula in order to ensure that optimum learning is achieved via e-learning, while at the same time enhancing our use, knowledge and understanding of the e -learning methods. Further research is warranted to assess if these changes have influenced objective educational outcomes like graduation rates or board scores.

Multi-stage intelligent smart lockdown using sir model to control covid 19

Corona Virus (COVID-19) is a contagious disease. Unless an effective vaccine is available, various techniques such as lockdown, social distancing, or business Standard operating procedures (SOPs) must be implemented. Lockdown is an effective technique for controlling the spread of the virus, but it severely affects the economy of developing countries. No single technique for controlling a pandemic situation has ever returned a promising result;therefore, using a combination of techniques would be best for controlling COVID-19. The South asian association of regional corporation (SAARC), region contains populous and developing countries that have a unique social-cultural lifestyle that entails a higher rate of contact and R0. The per-capita income and economic conditions of these countries are dismal in comparison with those of advanced countries. With no lockdown policy, their healthcare systems would be unable to provide support. In this study, an intelligent smart lockdown strategy is proposed, which is dynamically implemented with the Susceptible, infectious, or recovered (SIR) model by calculating the R0 value after a certain number of days to implement multi-stage lockdowns with social distancing and SOPs for conducting business. Only 4.28% of the population of SAARC countries would be affected by COVID-19 after July 22, 2021 under the proposed strategy. Nearly 38% of the population would be affected after March 8, 2021 without lockdowns, whereas 18% of the population would be affected according to the simple SIR model after May 30, 2021. Furthermore, less than 1% of the population would be affected after April 13, 2021 under full lock-down and recession. Thus, the proposed strategy shows promising long-term results for controlling COVID-19 without negatively affecting the economy. © 2021, Tech Science Press. All rights reserved.

Molecular evidence and hematological alterations associated with the occurrence of coronavirus in domestic dogs in Pakistan.

Canine Enteric Coronavirus (CCoV) is one of the major enteric pathogen affecting dogs. This study aims to investigate the molecular prevalence, phylogenetic analysis, associated risk factors, and haemato-biochemical alterations in Canine Coronavirus in dogs in district Lahore, Pakistan. 450 fecal samples were collected from symptomatic dogs originating from various pet-clinics and kennels during 2018-2019. Samples were initially analyzed by sandwich lateral flow immunochromatographic assay and then further processed by RT-PCR (reverse transcriptase polymerase chain reaction) targeting the M gene followed by sequencing. RT-PCR based positive (n=20) and negative (n=20) dogs were samples for their blood for the haemato-biochemical analysis. A questionnaire was used to collect data from pet owners, in order to analyze the data for risk factors analysis by chi square test on SPSS. The prevalence of CCoV was 35.1%, and 23.8 % through Sandwich lateral flow immunochromatographic and RT-PCR respectively. Various risk factors like breed, age, sex, vomiting, diarrhea, sample source, body size, cohabitation with other animals, living environment, food, deworming history, contact with other animals or birds feces, and season were significantly associated with CCoV. The CCoV identified in Pakistan were 98% similar with the isolates from China (KT 192675, 1), South Korea (HM 130573, 1), Brazil (GU 300134, 1), Colombia (MH 717721, 1), United Kingdom (JX 082356, 1) and Tunisia (KX156806). Haematobiochemical alterations in CCoV affected dogs revealed anaemia, leucopenia, lymphopenia, neutrophilia, and decreased packed cell volume, and a significant increase in alkaline phosphate and alanine transaminase. It is concluded that infection with canine coronavirus appears widespread among dog populations in district Lahore, Pakistan. This study is the first report regarding the molecular detection and sequence analysis of CCoV in Pakistan.