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The Influence of Cultural Factors on Trust in Automation
Principal Investigator(s): View help for Principal Investigator(s) Shih-Yi Chien, University of Pittsburgh; Michael Lewis, University of Pittsburgh; Katia Sycara, Carnegie Mellon University; Jyi-Shane Liu, National ChengChi University; Asiye Kumru, Özyeğin University
Version: View help for Version V1
Project Citation:
Chien, Shih-Yi, Lewis, Michael, Sycara, Katia, Liu, Jyi-Shane, and Kumru, Asiye. The Influence of Cultural Factors on Trust in Automation. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-04-25. https://doi.org/10.3886/E100532V1
Project Description
Summary:
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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.
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.
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.
Scope of Project
Subject Terms:
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Trust in automation ;
cultural influences;
information transparency;
alarm systems;
Human Factors;
Human computer interaction;
workload;
user study;
scale development
Geographic Coverage:
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USA,
Turkey,
Taiwan
Data Type(s):
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aggregate data;
experimental data;
survey data
Methodology
Sampling:
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Multistage, Corss-cultural data collection, Clustered sampling design
Data Source:
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Student Participants, School of Information Sciences, University of Pittsburgh, 2013-2016
Student Participants, Department of Computer Science, National ChengChi University, Taipei, Taiwan, 2013-2016
Student Participants, Department of Psychology, Ozyegin University, Istanbul, Turkey, 2013-2016
Turkers in Amazon Mechanical Turk, 2013-2014
Collection Mode(s):
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cognitive assessment test;
on-site questionnaire;
self-enumerated questionnaire;
web-based survey
Scales:
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A Likert-type scale was used
Unit(s) of Observation:
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School of Information Sciences, University of Pittsburgh,
Department of Computer Science, National ChengChi University, Taipei, Taiwan,
Department of Psychology, Ozyegin University, Istanbul, Turkey,
Amazon Mechanical Turk
Geographic Unit:
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Country
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