Measuring Individual Differences in the Perfect Automation Schema

Merritt, Stephanie M.; Unnerstall, Jennifer L.; Lee, Dong Hoon; Huber, Kelli

doi:10.1177/0018720815581247

Cited by 88 publications

(55 citation statements)

References 24 publications

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“…In regard to the perfect automation schema scales, we replicated the findings of Merritt et al (2015) that the reverse-worded items had poor psychometric properties. So, consistent with Merritt et al (2015), we dropped those items.…”

Section: Resultssupporting

confidence: 69%

“…This is a 10-item measure developed by Merritt et al (2015). It is divided into two subscales: high expectations, which measures participants’ expectations of automation performance and all-or-none thinking, which classifies participants based on their tendency to assume a machine to be broken if it doesn’t function properly.…”

Section: Methodsmentioning

confidence: 99%

“…Three of these, propensity to trust machines, perfect automation schema, and preference for multitasking, were selected in order to provide evidence of discriminant validity. Propensity to trust machines and perfect automation schema have been examined in the trust in automation literature and have shown to be substantially distinct from each other (Merritt et al, 2015). To our knowledge, preference for multitasking has not yet been examined in the context of automation use but was included here due to its relevance to attention allocation.…”

Section: Introductionmentioning

confidence: 99%

“…This construct was developed by Dzindolet et al (2002), who observed that users with higher perfect automation schemas demonstrated greater declines in trust following automation errors, compared to users with lower perfect automation schemas. Based on a review of the literature, Merritt et al (2015) developed a two-factor measure with factors reflecting high expectations for automation performance and all-or-none beliefs about automation performance, the latter referring to a belief that automation either works perfectly or not at all. They found low correlations between these two factors, suggesting that they are two separate constructs rather than two elements of the same construct.…”

Section: Introductionmentioning

confidence: 99%

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Automation-Induced Complacency Potential: Development and Validation of a New Scale

et al. 2019

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Complacency, or sub-optimal monitoring of automation performance, has been cited as a contributing factor in numerous major transportation and medical incidents. Researchers are working to identify individual differences that correlate with complacency as one strategy for preventing complacency-related accidents. Automation-induced complacency potential is an individual difference reflecting a general tendency to be complacent across a wide variety of situations which is similar to, but distinct from trust. Accurately assessing complacency potential may improve our ability to predict and prevent complacency in safety-critical occupations. Much past research has employed an existing measure of complacency potential. However, in the 25 years since that scale was published, our conceptual understanding of complacency itself has evolved, and we propose that an updated scale of complacency potential is needed. The goal of the present study was to develop, and provide initial validation evidence for, a new measure of automation-induced complacency potential that parallels the current conceptualization of complacency. In a sample of 475 online respondents, we tested 10 new items and found that they clustered into two separate scales: Alleviating Workload (which focuses on attitudes about the use of automation to ease workloads) and Monitoring (which focuses on attitudes toward monitoring of automation). Alleviating workload correlated moderately with the existing complacency potential rating scale, while monitoring did not. Further, both the alleviating workload and monitoring scales showed discriminant validity from the previous complacency potential scale and from similar constructs, such as propensity to trust. In an initial examination of criterion-related validity, only the monitoring-focused scale had a significant relationship with hypothetical complacency ( r = -0.42, p < 0.01), and it had significant incremental validity over and above all other individual difference measures in the study. These results suggest that our new monitoring-related items have potential for use as a measure of automation-induced complacency potential and, compared with similar scales, this new measure may have unique value.

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Section: Resultssupporting

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automation-Induced Complacency Potential: Development and Validation of a New Scale

et al. 2019

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“…A multitude of factors within the system, the person and the situation have been linked to trust calibration processes (see Hancock et al, 2011 for an overview). On the side of the user, personality (e.g., Merritt and Ilgen, 2008;Merritt et al, 2015;Kraus et al, 2019a) as well as emotional states (e.g., Stokes et al, 2010;Merritt, 2011) have been found to influence trust formation and explain inter-individual differences in trust toward specific automated systems. In support of this notion, reviews and meta-analyses in human-machine interaction propose personality to be an important predictor for inter-individual differences in trust in automation (e.g., Lee and See, 2004;Hancock et al, 2011;Hoff and Bashir, 2015;Schaefer et al, 2016;Hock et al, 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Kraus¹,

Scholz²,

Meßner

et al. 2020

Front. Psychol.

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The advantages of automated driving can only come fully into play if these systems are used in an appropriate way, which means that they are neither used in situations they are not designed for (misuse) nor used in a too restricted manner (disuse). Trust in automation has been found to be an essential psychological basis for appropriate interaction with automated systems. Well-balanced system use requires a calibrated level of trust in correspondence with the actual ability of an automated system. As for these far-reaching implications of trust for safe and efficient system use, the psychological processes, in which trust is dynamically calibrated prior and during the use of automated technology, need to be understood. At this point, only a restricted body of research investigated the role of personality and emotional states for the formation of trust in automated systems. In this research, the role of the personality variables depressiveness, self-efficacy, self-esteem, and locus of control for the experience of anxiety before the first experience with a highly automated driving system were investigated. Additionally, the relationship of the investigated personality variables and anxiety to subsequent formation of trust in automation was investigated. In a driving simulator study, personality variables and anxiety were measured before the interaction with an automated system. Trust in the system was measured after participants drove with the system for a while. Trust in the system was significantly predicted by state anxiety and the personality characteristics self-esteem and selfefficacy. The relationships of self-esteem and self-efficacy were mediated by state anxiety as supported by significant specific indirect effects. While for depression the direct relationship with trust in automation was not found to be significant, an indirect effect through the experience of anxiety was supported. Locus of control did not show a significant association to trust in automation. The reported findings support the importance of considering individual differences in negative self-evaluations and anxiety when being introduced to a new automated system for individual differences in trust in automation. Implications for future research as well as implications for the design of automated technology in general and automated driving systems are discussed.

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Dynamics of Reliance on Algorithmic Advice

Gill,

Gillenkirch,

Ortner

et al. 2024

Behavioral Decision Making

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This study examines the dynamics of human reliance on algorithmic advice in a situation with strategic interaction. Participants played the strategic game of Rock–Paper–Scissors (RPS) under various conditions, receiving algorithmic decision support while facing human or algorithmic opponents. Results indicate that participants often underutilize algorithmic recommendations, particularly after early errors, but increasingly rely on the algorithm following successful early predictions. This behavior demonstrates a sensitivity to decision outcomes, with asymmetry: rejecting advice consistently reinforces rejecting advice again while accepting advice leads to varied reactions based on outcomes. We also investigate how personal characteristics, such as algorithm familiarity and domain experience, influence reliance on algorithmic advice. Both factors positively correlate with increased reliance, and algorithm familiarity significantly moderates the relationship between outcome feedback and reliance. Facing an algorithmic opponent increases advice rejection frequencies, and the determinants of trust and interaction dynamics differ from those with human opponents. Our findings enhance the understanding of algorithm aversion and reliance on AI, suggesting that increasing familiarity with algorithms can improve their integration into decision‐making processes.

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Measuring Individual Differences in the Perfect Automation Schema

Abstract: This measure may be used in future work to assess levels of PAS in users of automated systems in either research or applied settings.

Cited by 88 publications

References 24 publications

Automation-Induced Complacency Potential: Development and Validation of a New Scale

Automation-Induced Complacency Potential: Development and Validation of a New Scale

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Dynamics of Reliance on Algorithmic Advice

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