  A significant challenge to measuring human-automation trust is the amount of
construct proliferation, models, and questionnaires with highly variable
validation. However, all agree that trust is a crucial element of technological
acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a
consensus model for trust in human-automation interaction by performing a
meta-analysis of validated and reliable trust survey instruments. To accomplish
this objective, this work identifies the most frequently cited and
best-validated human-automation and human-robot trust questionnaires, as well
as the most well-established factors, which form the dimensions and antecedents
of such trust. To reduce both confusion and construct proliferation, we provide
a detailed mapping of terminology between questionnaires. Furthermore, we
perform a meta-analysis of the regression models that emerged from those
experiments which used multi-factorial survey instruments. Based on this
meta-analysis, we demonstrate a convergent experimentally validated model of
human-automation trust. This convergent model establishes an integrated
framework for future research. It identifies the current boundaries of trust
measurement and where further investigation is necessary. We close by
discussing choosing and designing an appropriate trust survey instrument. By
comparing, mapping, and analyzing well-constructed trust survey instruments, a
consensus structure of trust in human-automation interaction is identified.
Doing so discloses a more complete basis for measuring trust emerges that is
widely applicable. It integrates the academic idea of trust with the
colloquial, common-sense one. Given the increasingly recognized importance of
trust, especially in human-automation interaction, this work leaves us better
positioned to understand and measure it.
