Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework
It provides insights into the psychological mechanisms influencing adoption of autonomous AI agents, which is incremental as it applies an existing framework to a new context.
This study examined how cognitive perceptions of the OpenClaw AI system influence affective responses and behavioral intention to use it, finding that positive perceptions increase intention while negative ones reduce it, based on survey data from 436 users analyzed with structural equation modeling.
This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived personalisation, perceived intelligence, and relative advantage, while inhibiting factors include privacy concern, algorithmic opacity, and perceived risk. Survey data from 436 OpenClaw users were analysed using structural equation modelling. The results show that positive perceptions strengthen users' attitudes toward OpenClaw, which increase behavioural intention, whereas negative perceptions increase distrust and reduce intention to use the system. The study provides insights into the psychological mechanisms influencing the adoption of autonomous AI agents.