4,936 responses from 617 respondents to 8 items.
One of 2 tables from the same source; this one is the tam table. The others are listed under Related tables.
| Description | Eight-item technology acceptance and trust questionnaire about automotive AI (1-5), 617 online adults |
|---|---|
| Reference | Dorsch, J., & Deroy, O. (2025). The impact of labeling automotive AI as trustworthy or reliable on user evaluation and technology acceptance. Scientific Reports, 15, 1481. |
| DOI | 10.1038/s41598-025-85558-2 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/6un4h/ |
| Responses | 4,936 |
|---|---|
| Respondents | 617 |
| Items | 8 |
| Response categories | 5 |
| Responses per respondent | 8 |
| Responses per item | 617 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Internet-based |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Technology acceptance exit questionnaire (Dorsch & Deroy 2025) |
|---|---|
| Mean words per item | 12.375 |
| Mean characters per item | 76.250 |
| Mean characters per response | 10.200 |
| Flesch-Kincaid grade level | 10.714 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, dorsch_2025_automotive_ai.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age_band | Covariate | Demo: Age |
cov_drivers_licence | Covariate | Demo: DL |
cov_education | Covariate | Demo: Education |
cov_gender | Covariate | Demo: Gender |
cov_induction_check_pass | Covariate | made in the build script |
cov_language_check_pass | Covariate | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("dorsch_2025_ai_tam")
# Python
pip install irw
import irw
df = irw.fetch("dorsch_2025_ai_tam")
@article{Dorsch_2025, title={The impact of labeling automotive AI as trustworthy or reliable on user evaluation and technology acceptance}, volume={15}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-025-85558-2}, DOI={10.1038/s41598-025-85558-2}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Dorsch, John and Deroy, Ophelia}, year={2025}, month=Jan }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_6 v5.11 |
| Redivis dataset DOI | 10.57761/qahq-j855 |
| Manifest pin for this IRW version | v5.11 |
| Metadata source | irw_meta v44.0 |