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Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious disease caused by a new coronavirus that started first in Wuhan, China, and outspread worldwide with an unexpectedly fast speed. Flights have been canceled worldwide and transportation has been closed nationwide and across international borders. As a consequence, the economic activity has been stopped and stock markets have been dropped. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. On the other hand, such lockdown, along with minimal human mobility, has impacted the natural environment somewhat positively. Overall carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. A summary of the existing reports of the environmental impacts of COVID-19 pandemic are discussed and the important findings are presented focusing on several aspects: air pollution, waste management, air quality improvements, waste fires, wildlife, global migration, and sustainability.

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The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.

Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this paper include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.