1. Automation: not all trust is
created equal
Vicki Chiang
10/1/13

2. Not All Trust Is Created Equal: Dispositional and
History-Based Trust in Human-Automation Interactions
Stephanie M. Merritt and Daniel R. Ilgen
Human Factors: The Journal of the Human Factors and
Ergonomics Society 2008
Central goal of the paper is to show empirically the connections
between individual user perceptions and trust in automation
interactions
• User personality traits such as extraversion mediates trust
• It is understood that there is a correlation between trust in machines
and trust in other people, but individual traits are not parsed out
(Parasuraman and Miller, 2004 and Parasuraman and Riley, 1997)
• Trust is dynamic and can be divided into dispositional/initial trust
(interaction naïve) and post-task trust (after interaction)

3. 13 Hypotheses !
1: Initial trust is positively related to automation use on a subsequent task.
2: Propensity to trust machines is more strongly related to initial trust than to post-task trust.
3: Initial trust mediates the relationship between propensity to trust machines and automation use.
4: Extraversion is positively related to propensity to trust machines.
5: Initial trust in an automated system is positively associated with post-task trust.
6: Initial trust and post-task trust show evidence of discriminant validity.
7: Machine characteristics are more strongly related to post-task trust than to initial trust.
8: Automation use moderates the relationship of machine characteristics and post-task trust, such that
machine characteristics have a greater effect on post-task trust when use is high.
9: Significant variance in perceptions of machine characteristics exists even when actual machine
characteristics are constant.
10: Machine characteristics moderate the relationship of propensity to trust machines and post-task
trust.
11: Machine characteristics will moderate the relationship of propensity to trust machines and
perceptions of machine characteristics, such that more extreme levels of post-task trust will result when
both variables are either low or high.
12: Perceptions of machine characteristics account for additional variance in post-task trust beyond the
effects of the actual machine characteristics.
13: Perceptions of machine characteristics mediate the relationship between the interaction of
propensity to trust with actual machine characteristics and post-task trust.

4. Experimental design
• X-Ray screening task
o Computer based simulation of x-ray baggage screening
o X-ray images of suitcases with and without weapons
(limited to guns and knives) are shown on a monitor
o Study participants must quickly choose to “clear” or
“search” the baggage, based on whether or not they
believe that weapons are present.
o Participants are told that there is an automated machine
available to assist them (Automatic Weapons Detector or
AWD)

5. Experimental Design Continued
• 255 undergraduate student participants
o Study participants are separated into two groups: high
machine function and low machine function
o Study participants also answer a questionnaire to
determine their “propensity to trust machines”, extraversion
as well as control variables
o Participants receive AWD instruction and 1 minute demo
that included an error or breakdown in the AWD, after
which they rated their levels of trust.
o 20 minutes to screen as many bags as they could
o Lots of stats and correlations collected on participants

6. X-Ray Screening Task
Figure 1. X-Ray Screening
Task screenshot.
Downloaded from
hfs.sagepub.com by HFES
General on June 15, 2012
To simulate real world situations the operator must be quick
as well as accurate so a points system is assigned. Top scorer
wins $50.

7. Perceptions on machine characteristics for high and low AWD function groups
are statistically significant
AWD Machine Characteristics High vs Low Function

8. Factors and Correlations
Difference between initial trust and post-task trust is significant
Propensity to trust machines strongly correlates with initial trust, but does not
correlate with post-task trust.
Extraversion positively correlates to propensity to trust machines.

9. Automation use, machine characteristics and trust
• Automation use moderates the
relationship of machine characteristics
and post-task trust, such that machine
characteristics have a greater effect on
post-task trust when use is high.
Figure 2. Interaction between machine
characteristics and automation use predicting
post-task trust.
Figure 3. Interaction between propensity to trust
machines and machine characteristics predicting
post-task trust.
• Machine characteristics
moderate the relationship
of propensity to trust
machines and post-task
trust.

10. User Perceptions and Post-task Trust
• Hypothesis 12: Perceptions of machine
characteristics account for additional variance in
post-task trust beyond the effects of the actual
machine characteristics
• Hierarchical linear regression model was used and
“the effects of the actual machine characteristics
were significant (β = .51, p < .01)”.
• When perceptions were accounted for and added
to the model, it resulted in a large increase in
variance (∆R2 = .52, p < .01, 52%). Overall, the
model accounted for 78% of the variance in post-
task trust.

11. Conclusions
• The study empirically supports differences in trust in
human-automation interaction
o Trust is dynamic and affected by different factors
• User propensity to trust automation and machine
characteristics affect trust and vice versa
o Post-task trust is negatively affected when users with higher initial trust are
paired with a less reliable machine.
o Post-task trust is less effected by user propensity to trust machines with
more machine use
• User perceptions of automation are important and
measurable
o The same machine will be perceived differently by individual users
o Individual perceptions affect variance in trust by 52%

12. Future Research
• Research should take user’s individual perceptions
into account when studying trust
• Trust should be measured at different points in time
for greater accuracy
• Training on machines should be more individualized
to take user expectations and perceptions into
account
o Is this practical to implement?

13. Thank You