Characterizing Driver Trust in Vehicle Control Algorithm Parameters

Domeyer, Joshua E.; Venkatraman, Vindhya; Price, Morgan; Lee, John D.

doi:10.1177/1541931218621413

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Behavioral measures of trust are useful for understanding trust as a socio-cognitive construct between agents interacting in real-time ( Takayama, 2009 ; Schilbach et al, 2013 ) and can serve as inputs to models used to dynamically predict human behavior in specific task contexts ( Domeyer et al, 2018 ; Yang et al, 2021 ). However, behavioral measures are often tied to a specific context or experimental setup and are considered indirect measures of trust because it is possible to engage in trust-related behaviors without actually involving trust ( Chiou and Lee, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Alsaid,

Li,

Chiou

et al. 2023

Front. Psychol.

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IntroductionTrust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection.MethodsA review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (https://areen.shinyapps.io/Trust_explorer/).ResultsAt the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires’ items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process.DiscussionThe results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

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Section: Introductionmentioning

confidence: 99%

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Alsaid,

Li,

Chiou

et al. 2023

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Behavioral measures of trust are useful for understanding trust as a socio-cognitive construct between agents interacting in real-time (Schilbach et al, 2013;Takayama, 2009) and can serve as inputs to models used to dynamically predict human behavior in specific task contexts (Domeyer, Venkatraman, Price, & Lee, 2018;Yang, Schemanske, & Searle, 2021). However, behavioral measures are often tied to a specific context or experimental setup and are considered indirect measures of trust because it is possible to engage in trust-related behaviors without actually involving trust.…”

Section: Introductionmentioning

confidence: 99%

Measuring trust: A text analysis approach to compare, contrast, and select trust questionnaires

Areen¹,

Li²,

Chiou³

et al. 2022

Preprint

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Trust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection. A mapping review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (link to app). At the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires’ items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process. The results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

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Distributed dynamic team trust in human, artificial intelligence, and robot teaming

Huang

Cooke²,

Gutzwiller

et al. 2021

Trust in Human-Robot Interaction

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Characterizing Driver Trust in Vehicle Control Algorithm Parameters

Abstract: Human factors research in vehicle automation has focused on user interfaces such as performance feedback through visual and auditory displays (Blanco et al., 2015). Another approach is to use vehicle dynamics and vibrations as communicative tools for guiding attention (e.g.

Cited by 6 publications

References 14 publications

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Measuring trust: A text analysis approach to compare, contrast, and select trust questionnaires

Distributed dynamic team trust in human, artificial intelligence, and robot teaming

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