Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework

TL;DR

This paper explores how users' perceptions and emotions, influenced by system features and concerns, affect their intention to adopt OpenClaw, an autonomous AI system, using the CAC framework and survey data.

Contribution

It applies the Cognition--Affect--Conation framework to understand user adoption of autonomous AI, highlighting psychological factors and system perceptions affecting behavioural intention.

Findings

Positive perceptions increase behavioural intention.

Privacy concerns and opacity reduce trust and adoption.

Attitudes mediate the relationship between perceptions and intention.

Abstract

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.