Individual and well-being factors associated w chatbot usage: A 6-country study (journals.sagepub.com)

Researchers conducted a six-country survey study to identify which individual differences and well-being factors predict who uses chatbots and for what purposes. Using cross-sectional survey data and statistical modeling, the paper links demographics (age, education), digital literacy, personality traits, and mental-health indicators (e.g., loneliness, anxiety) to patterns of chatbot adoption, frequency, and task choice (information-seeking vs. emotional support). The analysis highlights both consistent predictors across countries and notable cross-national variation, indicating that socio-cultural context moderates how personal characteristics map onto chatbot use. For the AI/ML community, the findings matter for model design, evaluation, and deployment: user heterogeneity and well-being correlate with different use-cases (e.g., those reporting poorer well-being are more likely to seek emotional support), so personalization, safety guards, and culturally sensitive behavior must be prioritized. Methodologically, the study underscores the value of multi-country samples and controlling for demographic and psychological covariates when training and testing conversational agents. Practically, results suggest prioritizing accessible UX for older or less digitally literate users, integrating safeguards when chatbots are used for mental-health support, and ensuring datasets and benchmarks reflect cross-cultural diversity to avoid biased behavior in deployed systems.