10,078 responses from 1,008 respondents to 10 items.
One of 2 tables from the same source; this one measures Attitudes toward AI-enabled technology (support, trust, belief mechanisms). The others are listed under Related tables.
| Description | Support, trust, self-assessed understanding and seven belief statements about an AI-enabled technology (10 items, 1-5) after one randomly assigned vignette (control, or cars / social-media moderation / armed drones x AI enhancing / substituting for human judgment) for 1,008 US adults from a Lucid quota sample. |
|---|---|
| Reference | Kreps, S., George, J., Lushenko, P., & Rao, A. (2023). Exploring the artificial intelligence "Trust paradox": Evidence from a survey experiment in the United States. PLOS ONE, 18(7), e0288109. https://doi.org/10.1371/journal.pone.0288109 |
| DOI | 10.1371/journal.pone.0288109 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/EOYDJR |
| Responses | 10,078 |
|---|---|
| Respondents | 1,008 |
| Items | 10 |
| Response categories | 5 |
| Responses per respondent | 9.998 |
| Responses per item | 1007.800 |
| Density | 1.000 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific, Internet-based |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Attitudes toward AI-enabled technology (support, trust, belief mechanisms) |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | AI trust paradox survey experiment, second experiment (Kreps, George, Lushenko & Rao 2023) |
|---|---|
| Mean words per item | 23 |
| Mean characters per item | 141.500 |
| Mean characters per response | 7.400 |
| Flesch-Kincaid grade level | 9.346 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kreps_2023_ai_attitudes.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 |
treat | IRW standard Treatment group assignment in experimental studies. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | made in the build script |
cov_domain | Covariate | made in the build script |
cov_purpose | 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("kreps_2023_ai_attitudes")
# Python
pip install irw
import irw
df = irw.fetch("kreps_2023_ai_attitudes")
@article{Kreps_2023, title={Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0288109}, DOI={10.1371/journal.pone.0288109}, number={7}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Kreps, Sarah and George, Julie and Lushenko, Paul and Rao, Adi}, editor={Tung, Hans H.}, year={2023}, month=July, pages={e0288109} }
| 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 |