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To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs.

Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents.

The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not.

This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.

The purpose of the research is to analyze the factors that determine the intention of small- and medium-sized enterprises (SMEs) to adopt the Metaverse. For this purpose, the analysis of the effort expectancy and performance expectancy of the constructs in relation to business satisfaction is proposed.

The analysis was performed on a sample of 182 Spanish SMEs in the technology sector, using a PLS-SEM approach for development. For the confirmation of the model and its results, an analysis with PLSpredict was performed, obtaining a high predictive capacity of the model.

After the analysis of the model proposed in this research, it is recorded that the valuation of the effort to be made and the possible performance expected by the companies does not directly determine the intention to use immersive technology in their strategic behavior. Instead, the results obtained indicate that business satisfaction will involve obtaining information, reducing uncertainty and analyzing the competition necessary for approaching this new virtual environment.

The study represents one of the first approaches to the intention of business behavior in the development of performance strategies within Metaverse systems. So far, the literature has approached immersive systems from perspectives close to consumer behavior, but the study of strategic business behavior has been left aside due to the high degree of experimentalism of this field of study and its scientific approach. The present study aims to contribute to the knowledge of the factors involved in the intention to use the Metaverse by SMEs interested in this field.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.