How did COVID-19 impact working professionals – a typology of impacts focused on education sector

Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

Self-promotion and online shaming during COVID-19: A toxic combination

A public shaming frenzy has spread through social media (SM) following the instigation of lockdown policies as a way to counter the spread of COVID-19. On SM, individuals shun the idea of self-promotion and shame others who do not follow the COVID-19 guidelines. When it comes to the crime of not taking a pandemic seriously, perhaps the ultimate penalty is online shaming. The study proposes the black swan theory from the human-computer interaction lens and examines the toxic combination of online shaming and self-promotion in SM to discern whether pointing the finger of blame is a productive way of changing rule-breaking behaviour. A quantitative methodology is applied to survey data, acquired from 375 respondents. The findings reveal that the adverse effect of online shaming results in self-destructive behaviour. Change in behaviour of individuals shamed online is higher for females over males and is higher for adults over middle-aged and older-aged.

Acceptability of the COVID-19 contact-tracing app - Does culture matter?

During the pandemic, several countries deployed contact-tracing apps in order to contain or reduce the community spread of COVID-19. However, the success rate in terms of acceptance and use of these apps was reportedly low. Using information gathered from citizens across four European countries and the United States of America, this study explores the role of national culture in relation to the acceptance of these apps. Using partial least squares structural equation modelling (PLS-SEM), an analysis was undertaken of 3595 records from a cross-country survey dataset that is in the public domain and can be obtained from the Centre for Open Science (Study 1). This analysis was followed by another survey comprising 910 respondents (Study 2). The research model was then validated by using a qualitative approach and undertaking interviews with 51 participants from four countries (Study 3). The results confirmed the moderating role of national culture on the acceptability of the contact-tracing apps in relation to power-distance, masculinity, individualism, long-term orientation and indulgence in the pre-deployment phase (Study 1). There were, however, no significant differences in acceptability of the apps between countries in relation to uncertainty avoidance; and none of the hypotheses in Study 2 was supported. The study concludes that national culture is significant in terms of the acceptance of COVID-19 apps only during the pre-deployment phase; therefore attention is required with pertinence to pre-deployment strategies. Recommendations regarding how governments and public health institutions can increase the acceptability of contact-tracing apps have been highlighted.

Assessing the influence of COVID-19 protocol on online health information seeking intention of athletes in India

Purpose

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Design/methodology/approach

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

Findings

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

Originality/value

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.

Tourism sustainability during COVID-19: developing value chain resilience

The aim of this study is to evaluate the perceptions of prospective tourists through parameters by which the tourism and hospitality service sector can withstand the widespread implications to the sector as a result of the current pandemic. In turn this will lead to weighing up the means for recovery. The identified parameters are then classified, categorized and linked up with supply chain drivers to obtain a holistic picture that can feed into strategic planning from which the tourism and hospitality service sector could utilize to establish a resilient supply chain. This data can provide deep insight for both theorists and practitioners to utilize. It was found that reforming six supply chain drivers, whilst at the same time developing core competencies, is the central essence of a resilient supply chain within the tourism and hospitality business sector (who are at present working hard to counterbalance the many threats and consequent risks posed due to the pandemic).

Digital Adoption of Start-Ups With E-Governance Systems

This study aims to encourage the adoption intention of independent digitalization with e-governance among small investment start-ups. The study also analyzed the start-ups' adoption intention for digitalization by testing the significance of benefits and sacrifices associated with the digital platforms of e-governance. The current study also analyzed the moderating effect of digital support and awareness on digital adoption. The study's findings will also help the government in drafting policies to address the factors that positively impact the perceived value of digital adoption by developing a digital support system and further scaling up digital literacy, especially for start-ups. Overall, the study's findings will help the government understand the present status of start-ups' digitalization growth in India and the success rate of the Digital India mission. So, the e-governance system can be further strengthened accordingly.

The Factors of E-Government Service Quality in Kuwait During the Coronavirus Disease 2019 Pandemic

This study aims to explain factors that have influenced the e-government service quality in Kuwait during the Coronavirus Disease 2019 (COVID-19) pandemic. Data is collected data from 392 users of the e-government services in Kuwait. The analysis has revealed that having a functional e-government portal allows citizens to complete their transactions and depend heavily on using the e-government services. Moreover, trust, ease of use, and citizen's support were also significant in terms of affecting service quality. The findings also show reliability of content and appearance of information as significant factors. COVID-19 had moderating influence on the relationship between reliability, trust, and citizens' support with e-government service quality. These results imply that citizens care about the functionality of the e-government portal and that it allows them to conduct all the transactions and processes that they need. These findings are useful for government officials to understand the most important elements for e-government service quality and thus design services.

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Financial markets across the globe have seen rapid volatility and uncertainty owing to scary and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns, curfews, emergencies, etc. has stoked the high quantum of chaotic movement in equity markets and resulted in perplexed investor behaviour. It, therefore, is of paramount practical relevance to measure predictability of market fear at such a crucial juncture of time. Market fear can effectively be measured in terms of implied and historic volatility of equity markets. The present study chooses India VIX and 20-day rolling standard deviation of NIFTY returns to account for implied and historic volatility respectively during the ongoing COVID-19 timeline. Pertinent macroeconomic constructs, technical indicators and Google search volume index on meaningful keywords have been chosen as raw explanatory features for inspecting predictability. Boruta feature selection methodology has been used in a supervised manner to select significant features. State-Of-The-Art machine and deep learning algorithms namely Gradient Boosting (GB), Extra Tree Regression (ERT), Deep Neural Network (DNN), Long Short Term Memory Network (LSTM) are then used on processed feature set to scrupulously evaluate the quantum of predictability of said assets. The integrated predictive frameworks have been subjected to a battery of numerical and statistical checks to draw inferences. Additionally, Explainable AI frameworks have been used to analyse the nature of influence of respective features. Findings indeed suggest that despite exhibiting high degree of volatile traits, both India VIX and historic volatility can be predicted utilizing the proposed architectures effectively and serve practical actionable insights.

Working from Home During Covid-19: Doing and Managing Technology-enabled Social Interaction With Colleagues at a Distance

With the overnight growth in Working from Home (WFH) owing to the pandemic, organisations and their employees have had to adapt work-related processes and practices quickly with a huge reliance upon technology. Everyday activities such as social interactions with colleagues must therefore be reconsidered. Existing literature emphasises that social interactions, typically conducted in the traditional workplace, are a fundamental feature of social life and shape employees' experience of work. This experience is completely removed for many employees due to the pandemic and, presently, there is a lack of knowledge on how individuals maintain social interactions with colleagues via technology when working from home. Given that a lack of social interaction can lead to social isolation and other negative repercussions, this study aims to contribute to the existing body of literature on remote working by highlighting employees' experiences and practices around social interaction with colleagues. This study takes an interpretivist and qualitative approach utilising the diary-keeping technique to collect data from twenty-nine individuals who had started to work from home on a full-time basis as a result of the pandemic. The study explores how participants conduct social interactions using different technology platforms and how such interactions are embedded in their working lives. The findings highlight the difficulty in maintaining social interactions via technology such as the absence of cues and emotional intelligence, as well as highlighting numerous other factors such as job uncertainty, increased workloads and heavy usage of technology that affect their work lives. The study also highlights that despite the negative experiences relating to working from home, some participants are apprehensive about returning to work in the traditional office place where social interactions may actually be perceived as a distraction. The main contribution of our study is to highlight that a variety of perceptions and feelings of how work has changed via an increased use of digital media while working from home exists and that organisations need to be aware of these differences so that they can be managed in a contextualised manner, thus increasing both the efficiency and effectiveness of working from home.