The Antecedents of Consumer Satisfaction Toward OTT Platforms During COVID-19 Lockdown in India

The outbreak of COVID-19 has emerged as the biggest threat to human life. It has changed the entire lifestyle of human beings concerning their emotional stability and cognitive development. Enjoyment is one of the emotions and acts as a positive stimulus that a consumer used to feel and seeks for a balanced life. Since the nationwide lockdown was implemented on March 25, 2020, people have turned restless about how to enjoy themselves at home, as all of their options of going out were being shattered. It was the time when continuous innovations in the form of digital content through over-the-top (OTT) platforms flourished and provided an affordable and diversified entertainment source to consumers. These OTT services help consumers to view the contents via the Internet directly. Furthermore, it has drastically changed people's preferences toward diversified content based on their choices. This study aims to explore the various determinants affecting consumer satisfaction toward the OTT platforms amid COVID-19 crisis in India. The study's main findings revealed that among the demographic variables, age group of the consumer largely influences their satisfaction toward OTT platforms. Moreover, work from home, affordability, convenience, and content quality are the significant determinants affecting consumer satisfaction levels toward OTT platforms. The study is relevant to the current marketing scenario, as it provides useful insights for the content developers of the major OTT platforms such as Amazon Prime, Netflix, Disney+ Hotstar, Voot, and many others to enhance the consumer satisfaction in the terms of digital content consumption.

Deciphering drivers of ICT use in agricultural education

Information and Communication Technology (ICT) is an important element in the education scenario to prepare citizens for the future. Since, its inception factors influencing the use have not been studied more, hence an exploratory research study has been framed for finding those factors among the students of CCS Haryana Agricultural University, Hisar, Haryana during 2018-19. The data were collected from 200 respondents using structured interview schedule and analyzed using standard methodology. The findings revealed that the institutional factors were perceived highest with composite index value (CIV) of 70.18, followed by personal (67.35), technical (59.15) and economic constraints (47.33). Although, these factors could be overcome through implementing remedies suggested by students like;teaching should be through ICTs, followed by improved internet connection, training programme related to use of ICTs, etc. but an effective national level policy related to infrastructure facilities, software licensing, availability of high quality ICT gadgets at subsidized rates, free and regular training programmes, etc. are possible ways to deal with these factors especially during COVID-19 pandemic. The correlation and regression of different variables, i.e. age, education, family education, scientism, annual expenditure, mass media exposure, information seeking behaviour and risk orientation exhibited negative and significant effect with their perceived personal factors at 0.05 level of probability. Hence, the paper recommends that effective utilization of ICT depends not only upon the available ICT resources, but also effective utilization of the same.

Epidemiology of COVID-19 positive patients admitted in a dedicated COVID hospital

Introduction: The pandemic of Covid-19 was declared on 11 march 2020 by WHO.

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.