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

arXiv: https://arxiv.org/abs/2603.11455 · HTML: https://mlsanity.com/paper/2603.11455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [OpenClaw Agents on Moltbook: Risky Instruction Sharing and Norm Enforcement in an Agent-Only Social Network](https://mlsanity.com/paper/2602.02625/) (2026)
- [A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)](https://mlsanity.com/paper/2602.14364/) (2026)
- [Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agents](https://mlsanity.com/paper/2603.00902/) (2026)
- [AI, Opacity, and Personal Autonomy](https://mlsanity.com/paper/2210.08995/) (2022)
- [Execution Is the New Attack Surface: Survivability-Aware Agentic Crypto Trading with OpenClaw-Style Local Executors](https://mlsanity.com/paper/2603.10092/) (2026)
- [When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community](https://mlsanity.com/paper/2602.14477/) (2026)
- [The Importance of Distrust in AI](https://mlsanity.com/paper/2307.13601/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enabling and Inhibitory Pathways of University Students' Willingness to Disclose AI Use: A Cognition-Affect-Conation Perspective](https://mlsanity.com/paper/2604.21733/) (2026)

## Abstract (arXiv)

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.