Academic

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

arXiv:2603.11455v1 Announce Type: new 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.

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Yiran Du · · 1 min read · 10 views
#cs.AI

arXiv:2603.11455v1 Announce Type: new 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.

Executive Summary

This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation framework, analyzing the impact of cognitive perceptions on affective responses and subsequent behavioural intention. The research identifies enabling and inhibiting factors, including perceived personalisation, intelligence, and relative advantage, as well as privacy concerns, algorithmic opacity, and perceived risk. The results provide insights into the psychological mechanisms influencing the adoption of autonomous AI agents, highlighting the importance of positive perceptions in strengthening users' attitudes and increasing behavioural intention.

Key Points

  • ▸ The Cognition--Affect--Conation framework is used to examine users' behavioural intention to use OpenClaw
  • ▸ Enabling factors include perceived personalisation, intelligence, and relative advantage
  • ▸ Inhibiting factors include privacy concerns, algorithmic opacity, and perceived risk

Merits

Comprehensive framework

The study utilizes a comprehensive framework to examine the complex relationships between cognitive perceptions, affective responses, and behavioural intention

Demerits

Limited generalizability

The study's findings may not be generalizable to other AI systems or user populations, limiting the scope of the research

Expert Commentary

This study provides valuable insights into the psychological mechanisms driving users' behavioural intention to use OpenClaw, highlighting the importance of cognitive perceptions and affective responses in shaping adoption decisions. The findings have significant implications for the development and deployment of AI systems, emphasizing the need for transparency, personalisation, and user-centric design. However, further research is necessary to fully understand the complex relationships between these factors and to develop effective strategies for promoting AI adoption and trust.

Recommendations

  • ✓ Future studies should investigate the impact of AI systems on diverse user populations and contexts
  • ✓ Developers and regulators should prioritize transparency, accountability, and user experience in the development and deployment of AI systems

Sources

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