mlsanityData to 14 Oct 2026

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

arXiv 2603.11455 ↗First posted 12 Mar 2026cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

8 of 48 references.

References · 8

  1. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  2. 2026OpenClaw Agents on Moltbook: Risky Instruction Sharing and Norm Enforcement in an Agent-Only Social Networkcited by 20
  3. 2026A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)cited by 16
  4. 2026Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agentscited by 14
  5. 2022AI, Opacity, and Personal Autonomycited by 5
  6. 2026Execution Is the New Attack Surface: Survivability-Aware Agentic Crypto Trading with OpenClaw-Style Local Executorscited by 4
  7. 2026When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Communitycited by 3
  8. 2023The Importance of Distrust in AIcited by 1

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