Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System

Balfe, Nora; Sharples, Sarah; Wilson, John R.

doi:10.1177/0018720818761256

Cited by 43 publications

(26 citation statements)

References 42 publications

(72 reference statements)

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“…Beyond the affect measures, as the risk perceptions literature would predict (Slovic, , ), there was evidence that experience and knowledge shaped people's risk and benefit perceptions. People who had greater experience with vehicle automation technologies had lower risk and higher benefit perceptions as well as higher trust ratings of self‐driving cars, consistent with other literature on new vehicle technologies (Abraham et al., ; Balfe et al., ; Lee et al., ). Years of experience as a driver were not related to risk perception but were negatively related to benefit perceptions of and trust in self‐driving cars.…”

Section: Discussionsupporting

confidence: 85%

“…The concept of system reliability, or the belief that the system will work as described, is a form of trust in the technology; it is in essence the belief that the technology will deliver on its promised performance. Other research has also suggested that trust in the technology may be a predictor of acceptance (Abraham et al., ; Balfe, Sharples, & Wilson, ; Choi & Ji, ; Lee & See, ; Zmud, Sener, & Wagner, ).…”

Section: Introductionmentioning

confidence: 98%

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The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

et al. 2019

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Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 98%

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

et al. 2019

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“…However, the current results should not be interpreted as suggesting that transparency information is not useful, as the utility of transparency information is context dependent. Indeed, a recent real-world observational study [56] revealed that understandability of the automated system had the greatest impact on operators' trust in the system.…”

Section: A Limitationsmentioning

confidence: 99%

Agent Transparency and Reliability in Human–Robot Interaction: The Influence on User Confidence and Perceived Reliability

Wright

Chen

Lakhmani

2020

IEEE Trans. Human-Mach. Syst.

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Agent transparency is an important contributor to human performance, situation awareness (SA), and trust in humanagent teaming. However, agent transparency's effects on human performance when the agent is unreliable have yet to be examined. This paper examined how the transparency and reliability of an autonomous robotic squad member (ASM) affected a human observer's task performance, workload, SA, trust in the robot, and perceptions of the robot. In a 2 (ASM transparency) × 2 (ASM reliability) within-subject design experiment, participants monitored a simulated soldier squad that included an ASM as it traversed a simulated training environment, while concurrently monitoring the environment for targets. There was no difference in participants' performance on the target detection task, workload, or SA due to either ASM transparency or reliability. ASM reliability influenced participant trust and perceptions of the robot. Results suggest that reliability may be a stronger influence on the human's perceptions of the robot than transparency. Robot errors had a profound and lasting effect on the participants' perception of the robot's future reliability and resulted in reduced confidence in their assessments of the robot's reliability. These findings could have important implications for the continued use of automated systems when the user is aware of system errors.

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“…Likewise, different types of smart textiles (e.g., smart cushions, carpets, curtains) should be investigated as product examples in different application contexts (e.g., medical care, wellness, sports): As mentioned above, the adaption and integration of medical and care-related functions of smart textiles covers an important research field and should also be investigated in more detail in the future. From prior research we know that ease of using and the intention to use new technologies often declines with age, but that these effects can be compensated by higher perceived usefulness or a need to use the respective technologies [ 49 , 50 , 51 ]. Finally, this study was focused on two distinct, but rather generic scenarios with applications of smart interactive textiles and the investigated sample was young and well educated.…”

Section: Discussionmentioning

confidence: 99%

Let’s Talk about TEX—Understanding Consumer Preferences for Smart Interactive Textile Products Using a Conjoint Analysis Approach

Heek

Brauner

Ziefle

2018

Sensors

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Interactive textiles are reaching maturity. First technology augmented textiles in form of clothes and furnitures are becoming commercially available. In contrast to the close link between technological development and innovations, future users’ acceptance and usage of such interactive textiles has not been integrated sufficiently, yet. The current study investigates future users’ consumer behavior and acceptance of interactive textiles using a scenario-based conjoint analysis study, which was presented in an online questionnaire (n=324). Two prototypical interactive textiles were focused on: a smart jacket and a smart armchair. To assess the textile products, the participants had to choose the preferred product alternative consisting each of the acceptance-relevant factors “connectivity”, “input modality”, “feature range”, “usability”, and “ease of cleaning”and their respective levels. The results revealed that the “ease of cleaning” is the most important decision criterion for both textile devices (even more important for the smart jacket), followed by “feature range”, “connectivity”, and “usability”. In contrast, the “input modality” is perceived as least important. The study also identified user profiles based on the projected consumer behavior (“adopters”, “rejecters”, and “undecided”) for both products. Besides the differences in product evaluation and projected consumer behavior, the user groups are significantly influenced by the individual affinity to textiles (both products) and gender (smart jacket). The findings are used to derive design and communication guidelines referring to interactive textiles in order to incorporate users’ needs, wishes, and requirements into future products.

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Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System

Cited by 43 publications

References 42 publications

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

Agent Transparency and Reliability in Human–Robot Interaction: The Influence on User Confidence and Perceived Reliability

Let’s Talk about TEX—Understanding Consumer Preferences for Smart Interactive Textile Products Using a Conjoint Analysis Approach

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