Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system.

The growing popularity of social commerce may transform the purchase behaviour of consumers. It is the need of time to investigate the factors that impact the consumers’ purchase intention in the social commerce environment, especially in a developing country like Pakistan. The study is a drive to investigate the factors influencing the purchase intentions of consumers in social commerce. By employing social learning theory, this study proposed a theoretical model to explore the factors affecting consumers’ purchase intention and decision making. A structured questionnaire-based survey was conducted for data collection, and 232 valid responses were analysed using structural equation modelling (SEM) to validate the proposed research model. The results of the study concluded that social commerce constructs in the form of learning from forums and communities, learning from ratings and reviews, and learning from social advertisements were significant predictors of social support constructs. Furthermore, social support constructs such as emotional and informational support significantly contribute to predicting consumer’s purchase intentions in social networking sites. In addition, this study revealed that special focus was needed to build social commerce constructs and social support by the managers of social commerce sites to attain consumers’ purchase intention.

Historically, infectious diseases have been the leading cause of human psychosomatic strain and death tolls. This research investigated the recent threat of COVID-19 contagion, especially its impact among frontline paramedics treating patients with COVID-19, and their perception of self-infection, which ultimately increases their agonistic behaviour. Based on the stressor–strain–outcome paradigm, a research model was proposed and investigated using survey-based data through a structured questionnaire. The results found that the perceived threat of COVID-19 contagion (emotional and cognitive threat) was positively correlated with physiological anxiety, depression, and emotional exhaustion, which led toward agonistic behaviour. Further, perceived social support was a key moderator that negatively affected the relationships between agonistic behaviour and physiological anxiety, depression, and emotional exhaustion. These findings significantly contributed to the current literature concerning COVID-19 and pandemic-related effects on human behaviour. This study also theorized the concept of human agonistic behaviour, which has key implications for future researchers.