9. Automation and aging: Issues and considerations

Mouloua, Mustapha; Smither, Janan A.; Vincenzi, Dennis A.; Smith, Laura

doi:10.1016/s1479-3601(02)02011-8

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…This was surprising as there are well-known age differences in technology experience and attitudes (Czaja et al, 2006; Van Volkom et al, 2014). It was also surprising because there are well-documented age differences in attitudes and behavior toward automation (Mouloua et al, 2002).…”

Section: Resultsmentioning

confidence: 99%

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

et al. 2019

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Purpose Self-driving cars are an extremely high level of autonomous technology and represent a promising technology that may help older adults safely maintain independence. However, human behavior with automation is complex and not straightforward ( Parasuraman and Riley, 1997 ; Parasuraman, 2000 ; Rovira et al., 2007 ; Parasuraman and Wickens, 2008 ; Parasuraman and Manzey, 2010 ; Parasuraman et al., 2012 ). In addition, because no fully self-driving vehicles are yet available to the public, most research has been limited to subjective survey-based assessments that depend on the respondents’ limited knowledge based on second-hand reports and do not reflect the complex situational and dispositional factors known to affect trust and technology adoption. Methods To address these issues, the current study examined the specific factors that affect younger and older adults’ trust in self-driving vehicles. Results The results showed that trust in self-driving vehicles depended on multiple interacting variables, such as the age of the respondent, risk during travel, impairment level of the hypothesized driver, and whether the self-driving car was reliable. Conclusion The primary contribution of this work is that, contrary to existing opinion surveys which suggest broad distrust in self-driving cars, the ratings of trust in self-driving cars varied with situational characteristics (reliability, driver impairment, risk level). Specifically, individuals reported less trust in the self-driving car when there was a failure with the car technology; and more trust in the technology in a low risk driving situation with an unimpaired driver when the automation was unreliable.

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Section: Resultsmentioning

confidence: 99%

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

et al. 2019

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“…They reported that younger participants were more willing to use automated vehicles to transport their unaccompanied child(ren), possibly because they have fewer concerns about the reliability of automated technologies. However, it should be noted that there is considerable debate within the literature regarding the relationship between age and propensity for technology adoption (Mouloua, Smither, Vincenzi, & Smith, 2002).…”

Section: Discussionmentioning

confidence: 99%

Key factors associated with Australian parents’ willingness to use an automated vehicle to transport their unaccompanied children

Koppel

Lee

Mirman

et al. 2021

Transportation Research Part F: Traffic Psychology and Behaviou

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“…Several studies have revealed that older people tend to overtrust automation (Donmez, Boyle, Lee, & McGehee, 2006;Fox & Boehm-Davis, 1998;Ho, Wheatley, & Scialfa, 2005;Pak, Fink, Price, Bass, & Sturre, 2012;Pak, McLaughlin, & Bass, 2014), and different reasons have been suggested to underlie this phenomenon: cohort effects and the associated differences in factors such as trust in technology (Pak et al, 2014), older people's lower confidence in their own ability and the higher workload they experience (Skitka et al, 1999), or cognitive changes such as reductions in working memory capacity (Ho et al, 2005). Age differences are more likely to emerge under high task load (Mouloua, Smither, Vincenzi, & Smith, 2002), and older adults may also need more time to calibrate their trust to the performance of the automated system (e.g., likelihood of false alarms vs. misses, Sanchez, Rogers, Fisk, & Rovira, 2014).…”

Section: Agementioning

confidence: 99%

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

Müller

Ullrich

Dessel

et al. 2018

Hum Ftrs & Erg Mfg Svc

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In the processing industry, frequent faults call for assistance in diagnosis, and case‐based reasoning (CBR) can provide solutions applied by other operators in the past. This study investigated whether salient case ratings promote an uncritical acceptance of solutions. In 2 experiments, subjects diagnosed faults with a simulated CBR system, and ratings were presented in graphical or verbal format. In most trials, the case with the highest rating provided the correct solution, while in catch‐trials, it did not. Graphical ratings were hypothesized to speed up solutions but discourage cross‐checking and lead to errors in catch‐trials. These hypotheses were not confirmed, even though Experiment 2 maximized the incentive of relying on case ratings. While graphical ratings led subjects to start with the most highly rated case, they did not impair situation analysis and accuracy. The results suggest that during fault diagnosis people are not easily misled into overtrusting a CBR system.

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9. Automation and aging: Issues and considerations

Cited by 8 publications

References 30 publications

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

Key factors associated with Australian parents’ willingness to use an automated vehicle to transport their unaccompanied children

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

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