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Restrictions imposed on freedom of movement and interaction with others due to the COVID-19 pandemic have had the effect of causing many people, especially students, to become addicted to social media. This study aims to investigate the effect of social media addiction on the academic performance of Sri Lankan government university students during the COVID-19 pandemic.

A convenience sampling technique was used to conduct a quantitative cross-sectional survey. The survey involved 570 respondents from nine state universities in Sri Lanka. The raw data from the completed questionnaires were coded and processed using SPSS for descriptive and inferential statistical analysis.

The findings of this study indicated that the overall time spent on social networking increased dramatically during COVID-19. Based on the results, this study found that there was no association between the time spent on social media and the academic performance of students before COVID-19 came on the scene. However, a significant association was found between the time spent on social media and students’ performance during the pandemic. The authors concluded that overblown social media use, leading to addiction, significantly negatively affects academic performance.

This study helps to understand the impact of social media use on the academic performance of students during COVID-19. Restrictions imposed by COVID-19 have changed the typical lifestyle of the students. Therefore, social media usage should be reassessed during the COVID-19 pandemic. The findings of the study will comprise these new insights, and they may well show how to adapt social media to contribute to academic work in meaningful ways.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.