How Social Chatbot Characteristics Shape Multidimensional Trust in Older Adults: Evidence from PLS-SEM and fsQCA

The potential of social chatbots show significant potential in advancing healthy aging, yet their effectiveness is limited by trust deficits among elderly users. Integrating multidimensional trust with the Stimuli-Organism-Response framework, this study examines how perceived chatbot characteristics shape cognitive trust (CT), emotional trust (ET), behavioral trust (BT), and usage behavior. Data from 292 older Chinese social chatbot users were analyzed using PLS-SEM and fsQCA. Results show that accuracy and transparency significantly enhance CT, warmth promotes ET, and CT and ET sequentially strengthen BT, which drives usage behavior. Anthropomorphism directly increases BT but does not significantly affect ET. Total effects identify anthropomorphism, ET, and CT as the strongest linear predictors of BT. FsQCA reveals three equifinal high-trust configurations: a cognitive-transparency-driven path, an emotional-centric synergy path, and a comprehensive compensation path. These findings clarify both net-effect and configurational mechanisms of trust formation and inform trustworthy chatbot design for older adults.