Mental health chatbots are increasingly deployed as scalable interventions, yet the relational mechanisms underpinning their effectiveness remain unclear. Drawing on prior research on digital therapeutic alliance, we operationalized a preliminary multi-dimensional instrument to capture perceptions of relational and functional dynamics in mental health chatbot interactions and conducted a four-week within-subjects study with 56 participants engaging with Wysa and Youper (two widely used CBT-based mental health chatbots). Through iterative factor refinement and regression modeling, we found that user-chatbot relationship formation is primarily driven by two factors: an affective factor, centered on emotional support, and a goal-oriented factor, centered on practical assistance. Conversational control contributed alongside these interpersonal factors, while trust (privacy, non-judgmentalness) and satisfaction emerged as correlated outcomes of supportive, effective interactions rather than standalone predictors. These findings advance models of the Digital Therapeutic Alliance by clarifying its underlying structure and highlighting design priorities for balancing empathy and efficacy in conversational agents.
ACM CHI Conference on Human Factors in Computing Systems