Ce questionnaire est un test psychologique qui mesure les états d'humeurs des athlètes. 

Il comporte de 34 items évalués sur une échelle de Likert à 5 éléments : Pas du tout (0), un peu (1), modérément (2), beaucoup (3), extrêmement (4).

Il permet de calculer un score total en faisant la somme des scores de chaque item ; il permet également de calculer des sous-scores : 

 This scale, of 14 items, is the short version of the 40 items trust perception scale-HRI.

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. 
Trust score is calculated by first reverse coding the "have errors", "unresponsive", and "malfunction" items, and then summing the 14 item scores and dividing by 14.


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, 34 items, scale aims to explain customers’ willingness to accept AI device use in service encounters.
It take into account sociual influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, emotion, willingness to accept the use of AI devices, objection to the use of AI devices.



Reference: Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157‑169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008 https://www.sciencedirect.com/science/article/pii/S0268401219301690?casa_token=JI-l_R98WhQAAAAA:UVHvXzORFhGyY19NIgIdPZgXp6jn8o8pL4pR1UgB1hvIMsTUWqTPGLRvzUtq-lJirD6-N9mn_HrTqA

This, 14 items, scale allows to evaluate different facets of trust, to understand how different factors about a decision-making process, and an AI model that supports that process, influences peoples’ perceptions of the trustworthiness of that process.

The evaluation of trust focused on several dimensions:

** Due to poor reliability (α = 0.11), we recommand to exclude Understandability from analysis.



Reference: Ashoori, M., & Weisz, J. D. (2019). In AI we trust? Factors that influence trustworthiness of AI-infused decision-making processes. arXiv preprint arXiv:1912.02675. https://arxiv.org/pdf/1912.02675

This scale, of 13 items, is a measurement tool for assessing transparency in recommender systems. It covers :
- Input: what data does the system use?
- Output: why and how well does an item fit one’s preferences?
- Functionality: how and why is an item recommended?
- Interaction: what needs to be changed for a different prediction?



Reference:  Hellmann, M., Bocanegra, D. C. H., & Ziegler, J. (2022). Development of an Instrument for Measuring Users' Perception of Transparency in Recommender Systems. Universität Duisburg-Essen. https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00075641/CEUR_WS_3124_Paper17.pdf

This, 40 items, scale measures employee trust for top management.

In question 30, you need te replace [company name] by the name of the company.



Reference:  Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of applied psychology, 84(1), 123.https://d1wqtxts1xzle7.cloudfront.net/17639018/oct_12_the_effect_of_the_performance_app..gement_a_field_quasi-experiment-libre.pdf?1390865536=&response-content-disposition=inline%3B+filename%3DThe_effect_of_the_performance_appraisal.pdf&Expires=1721299202&Signature=UOAKVTSUQpSNWLm39pVZ7gTVhw7cfLriq12fK--uJvajuPcpNSuFaHOXGsNtirlxD1LxkmYAd4Av2qJHZJ-fuFxVAAAXFvkgkojaPa9dMerkP9Aw~tKl1Cey5P6edDX2mpOVnE55sAZ74tdKexzhdavqDT2icNGaHEDaOFT6DTxbepVsI3PRLVMB~jEXCozOmy0M-eUy9Xo6Ffqk8UAIf6~-TLF5suVjxI-Aph0gLg6PNIiINbeO87zsZzjNTHj~umISVQVsjtfTmDgIhA~PS-CQZm14ifySCQInuz1hFOOIK-fAzf8emXumZaHNvDQF5O9ffBq-KyjtpFaydeW0aQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

This scale of 16 items takes into account 5 dimensions: risk perception, competency, reciprocity, benevolence, general trust; All of the items were positively worded except 
 for the risk perception scale, which was adapted as a negatively worded statement and reversed before analyzing the data.

 

The Humane-Computer Trust Scale (HCTS) was developed in the aim of researchers can use this in their research, therefore, to make it easy and less time consuming, we present the scale using placeholders [we refer to the brackets with dashes as presented above (—) as a placeholder]. 
 This placeholder needs to be filled in by the user of the scale.

 
 In items 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14 & 16, the placeholder needs to be replaced with the artefact with which the user of the scale intends to measure trust. For exemple, if the artefact is Amazon "Alexa", then the placeholder can simply be replaced by "Alexa".

 In items 6, 7, 8 & 15, the initial placeholder needs to be replaced by the artefact (just like the previous items). However, the second placeholder needs to be replaced with the functionality of the artefact or what the artefact is capable of doing. Again, if we use Alexa as an example, then for item 7, the first placeholder would be replaced by "Alexa" and the second placeholder can be replaced with any functionality of Alexa which the user of the scale wants to use to gauge trust perception. As an example, for item 7, second placeholder could be replaced by "providing personal assistance". So, the final sentence for item 7 using Alexa as an example would look like "I think that Alexa is competent and effective in providing personal assistance".

 
 To iterate, it doesn’t have to be "providing personal assistance". What goes in the placeholder depends on the context, study objectives and the motives of the researcher. Essentially, the placeholders are there for putting the name of the artefact and their context of use.

Reference:   Gulati, S., Sousa, S., & Lamas, D. (2019). Design, development and evaluation of a human-computer trust scale. Behaviour & Information Technology, 38(10), 1004-1015. https://d1wqtxts1xzle7.cloudfront.net/99473801/0144929X.2019.165677920230306-1-1u1dcgw-libre.pdf?1678089235=&response-content-disposition=inline%3B+filename%3DDesign_development_and_evaluation_of_a_h.pdf&Expires=1721296744&Signature=Ulu8sZrWl64h1QvVZDejsb0jrgr6dF88MzNgJ33fUTU4CtfoFTKAJngStWxdlS321VaLMPZglQxIMOxIU2cK-P6msKR632y77uib6FeERVRNRJ8GnJtbmA8fTV9etrFX7nN1Z582s1RejDbNXwp7kYz6aIYdRN1MDD1Zi1l5ABP90AimTBTHKFDeuiyiwlyR5lGBnyctzbXqm9GQNixYvlsVcwiOaWcyL221QK4c~oAfEMPBBb7kC50w5S-jfHIFm8Xpx5jITcstw2yL5qgAQrygxndODLB0j-WQgqbaLTVAYk7HbE4c8gaTdmHVWuwePSov7PMxq7kDPlztZvfC~g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

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