Accepted for/Published in: JMIR Human Factors
Date Submitted: Apr 27, 2024
Date Accepted: Sep 13, 2024
A new research model for AI well-being chatbot engagement: a survey study in China
ABSTRACT
Background:
Artificial intelligence (AI) chatbots have emerged as potential tools to assist individuals in reducing anxiety and providing and supporting well-being.
Objective:
This research aims to identify the factors that impact individuals' intention to engage and engagement behaviour with AI well-being chatbots being by using a novel research model to enhance service levels, improve user experience and mental health intervention effectiveness.
Methods:
We conducted an online questionnaire survey of adult well-being chatbot users in China via social media. Our survey collected demographic data, as well as a range of measures to assess relevant theoretical factors. Finally, 256 valid responses were obtained. The newly applied model was validated through the partial least squares structural equation modelling (PLS-SEM) approach.
Results:
The model explained 62.8% (R²) of the variance in intention to engage and 74.0% (R²) of the variance in engagement behaviour. Affect (β=.201, P=.002), social factors (β=.184, P=.007), and compatibility (β=.149, P=.033) were statistically significant to the intention to engage. Habit (β=.154, P=.012), trust (β=.253, P<.001), and intention to engage (β=.464, P<.001) were statistically significant to engagement behaviour.
Conclusions:
The new extended model provides a theoretical basis for studying users’ AI chatbot engagement behaviour. The research highlighted practical points for AI well-being chatbot designers and developers.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.