Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Published versio

“I Have to Praise You Like I Should?” The Effects of Implicit Self-Theories and Robot-Delivered Praise on Evaluations of a Social Robot

Recent research suggests that implicit self-theories—a theory predicated on the idea that people’s underlying beliefs about whether self-attributes, such as intelligence, are malleable (incremental theory) or unchangeable (entity theory), can influence people’s perceptions of emerging social robots developed for everyday use. Other avenues of research have identified a close link between ability and effort-focused praise and the promotion of individual implicit self-theories. In line with these findings, we posit that implicit self-theories and robot-delivered praise can interactively influence the way people evaluate a social robot, after a challenging task. Specifically, we show empirically that those endorsing more of an entity theory, indicate more favorable responses to a robot that delivers ability praise than to one that delivers effort praise. In addition, we show that those endorsing more of an incremental theory, remain largely unaffected by either praise type, and instead evaluate a robot favorably regardless of the praise it delivers. Together, these findings expand the state-of-the-art, by providing evidence of an interactive match between implicit self-theories and ability, and effort-focused praise in the context of a human-robot interaction