The Influence of Cultural Factors on Trust in Automation

Human interaction with automation is a complex
process that requires both skilled operators and complex system designs to
effectively enhance overall performance. Although automation has successfully
managed complex systems throughout the world for over half a century,
inappropriate reliance on automation can still occur, such as the recent
malfunction in Tesla autopilot mechanisms that resulted in a fatality. Research
has shown that trust, as an intervening variable, is critical to the
development of appropriate reliance on automated systems. Because automation
inevitably involves uncertainty, trust in automation is related to a
calibration between a user’s expectations and the capabilities of automation.
Prior studies suggest that trust is dynamic and influenced by both endogenous
(e.g., cultural diversity) and exogenous (e.g., system reliability) variables. While
a large body of work on trust in automation has accumulated over the past two
decades, a standard measure has remained elusive, with research relying on
short, idiosyncratically worded questionnaires. These challenges are
exacerbated for measuring trust in automation in non-Western cultures because
most research has been limited to North America and Western Europe.

<br><br>To determine how cultural factors affect
various aspects of trust in and reliance on automation, the present research
has developed a cross-cultural trust questionnaire and an air traffic control
simulator that incorporates a variety of scenarios identified from a review of
relevant literature. The measures and tasks have been validated by a
crowdsourcing system (Amazon Mechanical Turk), as well as through experimental
studies conducted in the U.S., Turkey, and Taiwan, with approximately 1000
participants. Over various phases of data collection and statistical
evaluations, a final 18-item Universal Trust in Automation (UTA) instrument was
identified that satisfies the stringent tests (including reliability and
validity tests and measurement invariance analysis), indicating that the
instrument is robust across national cultures and is effective in capturing
both predispositions to trust and trust that evolves through use of a system. The
findings reveal substantial cultural differences in human trust in automation,
which have a significant impact on the design, implementation, and evaluation
of automated systems to make them more trustworthy in determining the
appropriate trust calibration for optimized reliance across cultures.