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26 results for “user acceptance”
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
L3Pilot Global User Acceptance Survey, Second Phase Data
<p>The research leading to these results received funding from the European Commission Horizon 2020 programme under the project L3Pilot (L3Pilot.eu), grant agreement number 723051. The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the second phase of the survey with responses collected from 9 countries on five continents. This document contains information about the survey methodology and coding of the variables. For a detailed description of the first and second phase survey methodology, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’ by Nordhoff et al. (2021).</p> <p>If you use the dataset, please cite it as: L3Pilot (2023). L3Pilot Global User Acceptance Survey, Second Phase Data. <a href="https://doi.org/10.5281/zenodo.8389718">https://doi.org/10.5281/zenodo.8389718</a></p> <p>For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a></p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Supplementary materials for User Acceptance Factors of Usage-Based Insurance
<p><strong>Overview</strong></p> <p>This is supplementary material for the paper <em>User Acceptance Factors of Usage-Based Insurance</em>.</p> <p>What is included:</p> <ul> <li>01-survey-questionnaire.pdf - questionnaire used in the user study.</li> <li>02-ubci-video-explanations.mp4 - a video with an explanation about Usage-Based Car Insurance, which we provided to participants in our user study.</li> <li>03-SEM_Iteration-1-standardized.pdf - SEM analysis of the theoretical model, including the standardized coefficient.</li> <li>04-SEM_Iteration-1-unstandardized.pdf - SEM analysis of the theoretical model, including the unstandardized coefficient.</li> <li>05-SEM_Iteration-22-standardized.pdf - SEM analysis of the refined model, including the standardized coefficient.</li> <li>06-SEM_Iteration-22-unstandardized.pdf - SEM analysis of the refined model, including the unstandardized coefficient.</li> <li>07-linear-regression.pdf - interactors / moderators analysis.</li> </ul>
Supplementary materials for User Acceptance Factors of Usage-Based Insurance
<p><strong>Overview</strong></p> <p>This is supplementary material for the paper <em>User Acceptance Factors of Usage-Based Insurance</em>. Considering that the term UBI might not be well known to the general public, we added the word “car” to the original term, referring to it in survey, video, and data analysis as Usage-Based Car Insurance (UBCI).</p> <p>What is included:</p> <ul> <li>01-online-survey.pdf - questionnaire used in the user study.</li> <li>02-ubci-video-explanations.mp4 - a video with an explanation about Usage-Based Car Insurance, which we provided to participants in our user study.</li> <li>03-SEM_Iteration-1-standardized.pdf - SEM analysis of the theoretical model, including the standardized coefficient.</li> <li>04-SEM_Iteration-1-unstandardized.pdf - SEM analysis of the theoretical model, including the unstandardized coefficient.</li> <li>05-SEM_Iteration-22-standardized.pdf - SEM analysis of the refined model, including the standardized coefficient.</li> <li>06-SEM_Iteration-22-unstandardized.pdf - SEM analysis of the refined model, including the unstandardized coefficient.</li> <li>07-linear-regression.pdf - moderators analysis.</li> </ul>
L3Pilot Global User Acceptance Survey, First Phase Data
<p>The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the first phase of the survey with responses collected from 17 countries on five continents.</p> <p>Description.pdf contains information about the survey methodology and coding of the variables. For further information about the survey, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’.</p> <p>If you use the dataset, please cite it as: L3Pilot (2021). L3Pilot Global User Acceptance Survey, First Phase Data. https://doi.org/10.5281/zenodo.5255949</p> <p>For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a></p>
User Acceptability of a Nicotine Lactate Delivery System (P3L)
ClinicalTrials.gov study NCT02643693. IPD Sharing: NO. Countries: 1. Publications: 1.
User Acceptability of a Device-Based Opioid Overdose Intervention
ClinicalTrials.gov study NCT04530591. IPD Sharing: NO. Countries: 1. Publications: 1.
Accept Me as I Am or See Me Go: A Qualitative Analysis of User Acceptance of Self-Sovereign Identity Applications - Appendix
<p>Appendix to the Paper</p> <p>"Accept Me as I Am or See Me Go:<br> A Qualitative Analysis of User Acceptance of Self-Sovereign Identity Applications"</p> <p>presented at HICSS 2023</p>
Usability, Acceptability, User Experience, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals with Spinal Cord Injury
<p>This database originates from a study comparing two FES-cycling systems: the <strong>commercial BerkelBike Pro</strong> and a <strong>recumbent FES-bike prototype</strong> developed by the team at Politecnico di Milano. The aim of the study was to evaluate and compare the <strong>usability</strong>, <strong>acceptability</strong>, <strong>user experience</strong>, <strong>human-device interaction</strong>, and <strong>ergonomics</strong> of these two devices for individuals with spinal cord injury (SCI).</p> <p>The study involved 15 participants with SCI, covering a wide range of ages (18 to 65 years) and injury types (both complete and incomplete, at acute and chronic phases). Each participant underwent three sessions with both FES-cycling devices:</p> <ul> <li><strong>First session</strong>: Dedicated to setting up the system for each participant. For both the BerkelBike Pro and the FES-bike prototype, the system settings were tailored to meet individual needs, ensuring optimal comfort and function.</li> <li><strong>Second and third sessions</strong>: Focused on actual training, where participants used the devices to engage in cycling activities.</li> </ul> <p>At the end of the <strong>third session</strong>, participants completed a series of questionnaires to assess the usability, acceptability, user experience, and ergonomics of both devices. These questionnaires form the core of the study’s data collection and are central to understanding participants' interactions with the FES-cycling systems.</p> <p>The database is organized as follows:</p> <ol> <li><strong>Demographic data</strong>: The first page contains demographic information, including participants' age, gender, height, weight, time after injury, injury type (ASIA grade), and injury level.</li> <li><strong>PGWBI scores</strong>: The second page includes baseline data collected using the <strong>Psychological General Well-Being Index (PGWBI)</strong>, which assesses the participants' emotional and psychological state before engaging with the devices. The PGWBI consists of 22 questions grouped into 6 items: "anxiety", "depression", "positivity", "self-control", "health" and "vitality”. The responses are assessed on a 6-point scale ranging from 0 to 5. The score contributions for each question are then added together and transformed to achieve the final score that can range from 0 to 110.</li> <li><strong>SUS scores</strong>: The third page contains the results from the <strong>System Usability Scale (SUS)</strong>, a standard 10-item questionnaire that evaluates the usability of each FES-cycling system based on user ratings. The SUS is evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree, while 5 to strongly agree. The score contributions for each question are then added together and multiplied by 2.5 to achieve the final score that can range from 0 to 100, where higher scores indicate better usability.</li> <li><strong>TAM-3 scores</strong>: The fourth page presents data from the <strong>Technology Acceptance Model 3 (TAM-3)</strong>, which measures how participants perceive the ease of use and the usefulness of the devices. . The TAM-3 consists of 50 questions grouped into 14 items. These items include “perceived usefulness”, “perceived ease of use”, “self-efficacy”, “perception of external control”, “playfulness”, “anxiety”, “enjoyment”, “subjective norm”, “voluntariness”, “image”, “relevance”, “output quality”, “result demonstrability” and “behavioral intention”. The items are investigated using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree.</li> <li><strong>UEQ scores</strong>: The fifth page includes responses from the <strong>User Experience Questionnaire (UEQ)</strong>, evaluating participants' experience with the devices. The UEQ consists of 26 questions grouped in six items: “attractiveness”, “perspicuity”, “efficiency”, “dependability”, “stimulation” and “novelty”. Questions are scored using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree. Then scores per item are transformed using a scale ranging from -3 to +3, with +3 representing the most positive value (extremely good) and -3 the most negative one (horribly bad). Values between -0.8 and 0.8 represent a neutral evaluation of the corresponding scale, values > 0.8 represent a positive evaluation and values < -0.8 represent a negative one.</li> <li><strong>Custom questionnaire scores</strong>: The sixth page contains data from a <strong>custom-designed questionnaire</strong>, created specifically for this study to assess the ergonomics of the two FES-cycling systems, with particular attention to human-device interaction at both the physical and psychological levels. It consists of 12 questions covering four items: the transfer from/to the bikes and the initial tuning, bike comfort, its accessibility, and the ease of interaction. Questions are evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree/very uncomfortable, while 5 to strongly agree/very comfortable.</li> </ol> <p>This comprehensive data collection allows for a thorough comparison of the two FES-cycling systems in terms of user experience and overall acceptability in the SCI population.</p> <p>Please cite the following manuscript when using this database:<br>Nossa R, Biffi E, Sanna N, Diella E, Guanziroli E, Ferrari F, Ferrante S, Molteni F, Pedrocchi A, Tarabini M, Ambrosini E. Assessment of User Experience, Acceptability, Usability, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals With Spinal Cord Injury. Artif Organs. 2025 Apr 16. doi: 10.1111/aor.15007. Epub ahead of print. PMID: 40237144.</p>
A Randomized Acceptability and Safety Study of Suboxone Induction in Heroin Users (P05042)(COMPLETED)
ClinicalTrials.gov study NCT00604188. IPD Sharing: YES. Countries: 0. Publications: 1.
Acceptability of Simultaneous Screening for Viral Hepatitis B, C and HIV Among Drug Users in Non-conventional Structures
ClinicalTrials.gov study NCT05361603. IPD Sharing: NO. Countries: 1. Publications: 9.
Characterization of the Postural Habits of Wheelchair Users Analysis of the Acceptability of International Recommendations in the Prevention of Pressure Sores Risk by Using a Connected Textile Sensor
ClinicalTrials.gov study NCT04335942. IPD Sharing: NO. Countries: 1. Publications: 3.
BP Management System User Acceptance Testing
ClinicalTrials.gov study NCT04688450. IPD Sharing: NO. Countries: 1. Publications: 0.
User story, acceptance scenarios and prompts for LLM
<p>User story data, acceptance scenarios and prompts used for research into LLM adoption in Portuguese.</p>
Investigating the Acceptance and Performance of Low Energy Audio Streaming in Nucleus 8 and Kanso 3 Sound Processors by Experienced Cochlear Implant Users
ClinicalTrials.gov study NCT07262827. IPD Sharing: NO. Countries: 1. Publications: 0.
Non-interventional Study of Long-term Intrauterine Contraceptives Acceptability and User Satisfaction
ClinicalTrials.gov study NCT01590537. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Acceptability and User Perceptions of Artificial Intelligence-based Mobile Applications Adoption for Weight Management: A Sequential Explanatory Study
ClinicalTrials.gov study NCT05257239. IPD Sharing: NO. Countries: 1. Publications: 0.
Blinded User Study for the Evaluation of the Acceptability and Efficacy of One Medical Device in Venous Return in Comparison With a Control Group
ClinicalTrials.gov study NCT06395025. IPD Sharing: NO. Countries: 1. Publications: 0.
Outcomes and User Acceptance of the IntelliVue Alarm Advisor Software (USA)
ClinicalTrials.gov study NCT03347149. IPD Sharing: NO. Countries: 1. Publications: 0.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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