This scale, of 40 items, was developed to provide a means to subjectively measure trust perceptions over time and across robotic domains.

When the scale is used as a pre-interaction measure, the participants should first be shown a picture of the robot they will be interacting with or provided a description of the task prior to completing the pre-interaction scale. This accounts for any mental model effects of robots and allows for comparison specific to the robot at hand.
For post-interaction measurement, the scale should be administered directly following the interaction.
To create the overall trust score, 5 items must first be reverse coded (incompetent, unresponsive, malfunction, require frequent maintenance, have errors). All items are then summed and divided by the total number of items (40). This provides an overall percentage of trust score.

While use of the 40 items scale is recommended, a 14 items subscale can be used to provide rapid trust measurement specific to measuring changes in trust over time, or during assessment with multiple trials or time restrictions. This subscale is specific to functional capabilities of the robot, and therefore may not account for changes in trust due to the feature-based antecedents of the robot.



Reference:  Schaefer, K. E. (2016). Measuring trust in human robot interactions: Development of the “trust perception scale-HRI”. In Robust intelligence and trust in autonomous systems (pp. 191-218). Boston, MA: Springer US. https://d1wqtxts1xzle7.cloudfront.net/114178129/978-1-4899-7668-020240505-1-agu5dz-libre.pdf?1714934405=&response-content-disposition=inline%3B+filename%3DRobust_Intelligence_and_Trust_in_Autonom.pdf&Expires=1720525011&Signature=GiwTFX8RVqIoZ0hbY~O4fmCLoHrC4Zk6y-yviwJvsGZKm2pg7HiR3BNPjcyV4ROsD7TmigLEFsXIXf8UppjDyCRJWrbqyAFgpogdMr21TAWd9JakETZoju5qsSh8qgpmCQdR19PUJbtnb~DgcEdW7JpjjAYoY5A7h7aNXz97kUS0iHpRZaG-~1~ez4K82~5arEkL016b1QQUaaqk9Kk4A~j4qKbHg3fUST60QOKxwtzju1MQOscVJLX882NQmG03rhZ1jqAzb6VG4OnTjQL2hQP1eegcQk4j6TF1fTp0Q9idZo9LdQ7eq6yPro-8nCluVQ6w3bVUBc-45HPs43IjLg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA#page=198

This questionnaire is a combination of  7 point Likert scales, open form questionnaires to collect qualitative and quantitative user feedback, and checklist for trust between people and automation from Jian et al. (2000).

[ Jian, J. Y., Bisantz, A. M., & Drury, C. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International journal of cognitive ergonomics, 4(1), 53-71 ; https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed6a076ab7d43c27085d412108b98b93edbb1b00].

It was developed to evaluate the trust in a virtual agent + a speech recognition system. It is coupled with  Jian et al. (2000)' scale. This scale of 12 items measures the trust and distrust in automation.

In the first part of the questionnaire, you can replace the term [the virtual agent] by your own system.



Reference: Weitz, K., Schiller, D., Schlagowski, R., Huber, T., & André, E. (2021). “Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces, 15(2), 87-98.
https://link.springer.com/content/pdf/10.1007/s12193-020-00332-0.pdf

This, 24 items, scale evaluates the effect of anthropomorphism on investment decision-making with robo-advisor chatbots. It covers :
- Anthropomorphism
- Social presence
- Trusting beliefs
- Disposition to trust in technology


You can replace the term [robo-advisor chatbots] by your own system and the term [finanacial advise] by the type of advice the system gives you.



Reference:  Morana, S., Gnewuch, U., Jung, D., & Granig, C. (2020). The Effect of Anthropomorphism on Investment Decision-Making with Robo-Advisor Chatbots. In ECIS. https://www.researchgate.net/profile/Stefan-Morana/publication/341277570_The_Effect_of_Anthropomorphism_on_Investment_Decision-Making_with_Robo-Advisor_Chatbots/links/5eb7c5ba4585152169c14505/The-Effect-of-Anthropomorphism-on-Investment-Decision-Making-with-Robo-Advisor-Chatbots.pdf

The concept of AI literacy is used to determine user competence in using AI technology. This quantitative scale, of 12 items, allows to obtain accurate data regarding the AI literacy of ordinary users. This scale take into account the primary core constructs of AI literacy : awareness, usage, evaluation, and ethics.



Reference: Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 42(9), 1324-1337.https://www.researchgate.net/profile/Bingcheng-Wang/publication/360519116_Measuring_user_competence_in_using_artificial_intelligence_validity_and_reliability_of_artificial_intelligence_literacy_scale/links/6408741fb1704f343fb47955/Measuring-user-competence-in-using-artificial-intelligence-validity-and-reliability-of-artificial-intelligence-literacy-scale.pdf

This scale of 8 question assess trust in automation.
This XAI Trust Scale asks users directly whether they are confident in the XAI system, whether the XAI system is predictable, reliable, efficient, and believable. Most of the items are adapted from the Cahour-Fourzy scale, which has been shown to be reliable. The XAI Trust Scale incorporates items from other scales.

 You have to replace the notion [tool] with the tool that you want to evaluate.



Reference:  Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2023). Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Frontiers in Computer Science, 5, 1096257. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1096257/full

This questionnaire of 19 items measures the user's trust in an automation system. It takes into account 6 dimensions :
- Reliability/Competence
- Understandability/Predictability
- Propensity to Trust
- Intention of Developers
- Familiarity
- Trust in Automation



Reference: Körber, M. (2019). Theoretical considerations and development of a questionnaire to measure trust in automation. In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) Volume VI: Transport Ergonomics and Human Factors (TEHF), Aerospace Human Factors and Ergonomics 20 (pp. 13-30). Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-319-96074-6_2

The 26 scales of the questionnaire cover a comprehensive impression of user experience. Both classical usability aspects (efficiency, perspicuity, dependability) and user experience aspects (originality, stimulation) are measured.

Attractiveness
Overall impression of the product. Do users like or dislike it?

Perspicuity
Is it easy to get familiar with the product and to learn how to use it?

Efficiency
Can users solve their tasks without unnecessary effort? Does it react fast?

Dependability
Does the user feel in control of the interaction? Is it secure and predictable?

Stimulation
Is it exciting and motivating to use the product? Is it fun to use?

Novelty
Is the design of the product creative? Does it catch the interest of users?



Reference:   Laugwitz, B., Schrepp, M. & Held, T. (2008). Construction and evaluation of a user experience questionnaire. In: Holzinger, A. (Ed.): USAB 2008, LNCS 5298, pp. 63-76. https://www.ueq-online.org/

Questionnaire utilisable après avoir réalisé un scénario d'utilisation avec le dispositif ciblé.
Il est possible de comparer les 12 dimensions (qualités) du dispositif qui déterminent son acceptabilité.
Il est possible de modérer les résultats avec l'autoévaluation informatique (3 dimensions, page 2 du questionnaire)

Idéalement, il peut être utiliser pour comparer 2 dispositifs concurrents (ex. ancienne et nouvelle version du dispositif), après les avoir utilisés.
Il est bien adapté pour évaluer un (ou plusieurs) dispositifs après une expérience significative (ex. plusieurs jours ou semaines)

(Attention, l'item 30 est à valence négative, l'export des donné corrige automatiquement le sens de l'échelle)

Este questionário permite-lhe obter informações essenciais sobre os seus participantes (idade, sexo, etc.).

Os vários critérios avaliados através deste questionário são critérios tradicionais de segmentação da população.

No caso de estar a visar uma população específica, estes indicadores ajudá-lo-ão a determinar se os seus participantes se enquadram bem na sua amostra-alvo.
Estes elementos de informação também permitem verificar se a amostra é representativa da população em geral.

Se pretender estudar diferentes grupos de participantes, os dados sociodemográficos permitem-lhe criar as suas várias condições.