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Healthcare workers’ (HCWs’) job-related high exposure to Covid-19 virus arouses fear of Covid-19 among them. Based on the Theory of Mind (ToM), the study predicts that fears will lead to negative psychological (psychological distress) and behavioral (withdrawal intentions) outcomes. ToM is also used to identify social intelligence as a means to counter fear of Covid-19 on heightened psychological distress and increased withdrawal intentions.

To investigate the study design, a sample of 262 HCWs, including doctors, nurses and technicians, were surveyed using standardized questionnaires.

As predicted, Covid-19 fear led to increased withdrawal intentions with heightened psychological distress partially mediating the relationship. The alleviating role of social intelligence on the effects of Covid-19 was supported as high social intelligence reduced HCWs’ turnover intentions, with decreased psychological distress partially mediating the relationship.

Given the universality of the Theory of Mind (ToM), the findings of this study are likely to be generalizable to all pandemics. The study results support the increased application of ToM in organizational settings and have both theoretical and practical implications for health administrators. Based on study results, health administrators are exhorted to develop ToM-based mental models to understand and deal with the fear of contagious diseases. Health administrators can also increase HCWs’ social intelligence to deal with the negative perceptual and behavioral outcomes arising from the emotions aroused by the nature of their work.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.