ABSTRACT
The role of digitization and globalization have changed consumers' online buying behaviors, specifically in the times of the COVID-19 pandemic crisis. This seriously influences the online retail industry in developing countries that are already struggling to move toward digital trading through e-business. Pakistan being a developing country is no exception, and it is, therefore, pertinent to examine factors that contribute to digital trading. Employing theories of reasoned action and the technology acceptance model, this study aims to investigate how personal innovativeness and perceived usefulness impact consumers' online purchase intentions through a serial mediational model. The data were collected through an online survey from 410 respondents. Structural Equation Modeling (SEM) was used to test the proposed model. This study showed significant results for the direct effect of personal innovativeness and perceived usefulness on online purchase intentions as well as the indirect serial effect via internet browsing and attitude toward online purchasing. The study results have some important practical implications for selling firms, especially in the times of COVID-19. The study suggests that online retailers should be more responsive to the aforementioned factors to facilitate consumers to spend more time browsing, which influences consumers' interest and intention to make online purchases. As the social distancing and lockdown approaches were implemented in Pakistan and other parts of the world, the trend toward online purchases has increased. Due to this shift in the overall purchasing behavior of consumers and the potential for strong growth in e-commerce, organizations need to consider the post-COVID situation to expand their business in an online platform for addressing the future pandemic crisis.
ABSTRACT
The year 2020 marked an important moment when the COVID-19 pandemic promoted Internet as a necessity even more than before, especially for school activities and businesses. This increased usage emphasized the importance of cybersecurity, a frequently overlooked subject by the common users, which in return plays a crucial role in safe Internet browsing. This paper introduces an approach grounded in Natural Language Processing techniques to identify the main trends in security news and empowers the analysis of vulnerable products, active attacks, as well as existing methods of defence against new attacks. Our dataset consists of 2264 news articles published on cybersecurity dedicated websites between January 2017 and May 2021. The RoBERTa language model was used to compute the texts embeddings, followed by dimensionality reduction techniques and topic clustering methods. Articles were grouped into approximately 20 clusters that were thoroughly evaluated in terms of importance and evolution. © 2021 IEEE.