Empirically And Theoretically Driven Scales On Automation Trust: A Multi-Level Confirmatory Factor Analysis

Long, Shelby K.; Satô, Tetsuya; Millner, Nick; Loranger, Ray; Mirabelli, Julianna; Xu, Violet; Yamani, Yusuke

doi:10.1177/1071181320641440

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Data-driven approaches have been proven useful in expanding knowledge and extracting scientific relationships years in advance of their discovery ( Tshitoyan et al, 2019 ). Yet, trust is a complex, multifaceted construct and future work should incorporate a theory-driven approach to safeguard the theoretical underpinnings of trust, expand trust theory, and build on existing measures ( McCroskey and Young, 1979 ; Long et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…The words in light gray are the most common in the trust questionnaires, while the rest of the words are their closest neighbors in the high-dimensional GloVe embeddings. Alsaid et al 10.3389/fpsyg.2023.1192020 Frontiers in Psychology 16 frontiersin.org and build on existing measures (McCroskey and Young, 1979;Long et al, 2020). Sixth, our analyses leveraged one method of text analysis: word embeddings.…”

Section: Limitations and Further Researchmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Limitations and Further Researchmentioning

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

“…Data-driven approaches have been proven useful in expanding knowledge and extracting scientific relationships years in advance of their discovery (Tshitoyan et al, 2019). Yet, trust is a complex, multifaceted construct and future work should incorporate a theory-driven approach to safeguard the theoretical underpinnings of trust, expand trust theory, and build on existing measures (Long et al, 2020;McCroskey & Young, 1979).…”

Section: Limitations and Further Researchmentioning

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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“…In addition to trust in autonomous vehicles, much research focuses on independent vehicle interaction systems. Long et al (2020) found that personalization can increase trust in automation and thus increase user willingness to use the system by accepting and understanding each driver’s behavior. Anthropomorphic messages have been shown to promote the perception of AVs as social subjects and to enhance trust in these cars ( Niu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Motivation to interaction media: The impact of automation trust and self-determination theory on intention to use the new interaction technology in autonomous vehicles

Xie

Zhou²,

Chan³

et al. 2023

Front. Psychol.

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IntroductionThis research investigated the effects of three psychological needs (competence, autonomy, and relatedness) of self-determination theory (SDT) and automation trust on the intention of users to employ new interaction technology brought by autonomous vehicles (AVs), especially interaction mode and virtual image.MethodThis study focuses on the discussion from the perspective of psychological motivation theory applied to AV interaction technology. With the use of a structured questionnaire, participants completed self-report measures related to these two interaction technologies; a total of 155 drivers’ responses were analyzed.ResultThe results indicated that users’ intentions were directly predicted by their perceived competence, autonomy, and relatedness of SDT and automation trust, which jointly explained at least 66% of the variance in behavioral intention. In addition to these results, the contribution of predictive components to behavioral intention is influenced by the type of interaction technology. Relatedness and competence significantly impacted the behavioral intention to use the interaction mode but not the virtual image.DiscussionThese findings are essential in that they support the necessity of distinguishing between types of AV interaction technology when predicting users’ intentions to use.

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Empirically And Theoretically Driven Scales On Automation Trust: A Multi-Level Confirmatory Factor Analysis

Cited by 8 publications

References 22 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

Motivation to interaction media: The impact of automation trust and self-determination theory on intention to use the new interaction technology in autonomous vehicles

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