Opinions or Algorithms: An Investigation of Trust in People Versus Automation in App Store Security

Schuster, David M.; Still, Mary L.; Still, Jeremiah D.; Lim, Ji Jung; Feria, Cary S; Rohrer, Christian

doi:10.1007/978-3-319-20376-8_37

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Finally, the sample size in the conditions of the TC phase, in which feedback and reliability influences on human trust were investigated, is relatively small to draw generalizable conclusions. This might also be the reason why we were not able to find any statistically significant differences between men and women in trust which does not conform with previous studies where women reported higher trust in Gallimore et al (2019) and lower trust in Schuster et al (2015) than men. A larger sample size would better represent the population allowing for the use of parametric tests and enhancing the power and generalizability of the results.…”

Section: Discussioncontrasting

confidence: 98%

“…We will refer to the first group as G1 and the second one as G2 . Different research work suggests that there is a difference between men and women in terms of trust [e.g., trust games in economics ( Dittrich, 2015 ), human robot interaction ( Gallimore et al, 2019 ), and human automation interaction ( Schuster et al, 2015 )]. Therefore, we made sure that the number of men and women was equal in the groups.…”

Section: Methodsmentioning

confidence: 99%

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Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

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Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.

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Section: Discussioncontrasting

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

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“…This setting allowed for full data collection and was expected to still induce time pressure despite the lack of consequence for slow responses. Participants performed 24 experimental trials; this number of trials was modeled after that was used by Schuster and colleagues (2015), though the current study added one attentional check trial. After completing all 25 trials, participants completed the exit survey, described in Materials, and were compensated.…”

Section: Methodsmentioning

confidence: 99%

Framing Security Under Time Pressure: Brand Familiarity Matters for Mobile Application Choices

Chen,

Parker

2023

Journal of Cognitive Engineering and Decision Making

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The current study examined the effects of security score framing, time pressure, and brand familiarity on mobile application choices. Past research has found the framing of safety versus risk scores affects how potential risks for mobile apps are communicated to users. Both time pressure and brand familiarity have been shown to affect consumers’ purchase behaviors but not yet for app-selection decisions. The current study examined the effects of time pressure and brand familiarity on the effectiveness of risk displays (framed as safety or risk) for mobile apps. Participants were shown screenshots of various apps with these factors manipulated, and they were to choose one out of six apps. Our findings indicate that users rely heavily on brand familiarity when choosing apps, which could lead to insecure decisions. Additionally, security scores guided app choices toward more secure apps when framed as safety than when framed as risk, although this advantage was only evident without time pressure and disappeared under time pressure. The design implications call for more careful screening and user education about the potential risks associated familiar apps, as well as the need of new security design solutions to help users under time pressure.

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“…Finally, user ratings were controlled by assigning a rating of four stars across all apps to minimize their effect on app choices. Participants were provided 24 experimental trials (Schuster et al, 2015) and one catch trial. The catch trial consisted of five apps with low brand familiarity and low (high) safety (risk) scores and one app with high brand familiarity and a high (low) safety (risk) score.…”

Section: Design Of Experimentmentioning

confidence: 99%