09419043.pdf (3.49 MB)
Download file

Generalised anxiety disorder and depression on implicit and explicit trust tendencies towards automated systems

Download (3.49 MB)
This paper explores whether generalised anxiety disorder (GAD) and depression have any effect on an individual’s explicit general propensity to trust automated systems (trust that is unspecific to any one automated system) and whether those that do have these disorders have an implicit bias towards automated systems over other humans. The human-automated system literature to date has discovered that individual differences in humans, such as self-confidence, mood, and personality types, can influence the human-automated system relationship through human trust and reliance attitudes and behaviour. However, whether suffering from a mental disorder influences an individual’s attitudes towards automated systems generally is yet to be explored. In this study, 184 UK university students responded to online experiments between December 2019 – January 2020 and were subjected to the cultural trust instrument survey and the implicit association test in a between-subjects design to measure their general propensity to trust and implicit association towards automated systems respectively. A two-way ANOVA was performed to evaluate GAD × depression interaction effects on the dependent variables. Results suggest there was a significant interaction between GAD and depression regarding propensity to trust automated systems but they have little to no influential effect on mean implicit association test scores. Furthermore, those without depression showed a significantly higher trust score when they also had GAD. It can be concluded that GAD and depression have potential critical influence over human-automated system trust, thus creating potential issues with misuse and disuse and must be accounted for in automated system design.


NPIF EPSRC Doctoral - Loughborough University 2017

Engineering and Physical Sciences Research Council

Find out more...

Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)

Engineering and Physical Sciences Research Council

Find out more...



  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Access






Institute of Electrical and Electronics Engineers (IEEE)


VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date


Publication date


Copyright date









Dr Mey Goh. Deposit date: 27 April 2021