612 responses from 204 respondents to 3 items.
One of 11 tables from the same source; this one measures Behavioural intention to use generative AI (UTAUT2). The others are listed under Related tables.
| Description | Behavioural intention to use generative AI, 3 items, 1-7, 204 students at Portuguese higher-education institutions (European Portuguese), 2025-2026; cov_gender, cov_age, cov_study_year, cov_field, cov_prior_ai_experience, cov_ai_use_frequency. |
|---|---|
| Reference | Pinho, C. S. B. (2026). Generative AI Use, AI Literacy and Self-Efficacy among Portuguese Higher-Education Students [Data set]. figshare. https://doi.org/10.6084/m9.figshare.33296244.v1 |
| Licence | CC BY 4.0 |
| Source data | https://figshare.com/articles/dataset/33296244 |
| Responses | 612 |
|---|---|
| Respondents | 204 |
| Items | 3 |
| Response categories | 7 |
| Responses per respondent | 3 |
| Responses per item | 204 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, General/non-specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | por |
| construct name | Behavioural intention to use generative AI (UTAUT2) |
This codebook is reconstructed by the IRW from the IRW data standard. 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
| Respondent covariates | ||
cov_age | Covariate | not traced |
cov_ai_use_frequency | Covariate | not traced |
cov_field | Covariate | not traced |
cov_gender | Covariate | not traced |
cov_prior_ai_experience | Covariate | not traced |
cov_study_year | Covariate | not traced |
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("pinho_2026_behavioral_intention")
# Python
pip install irw
import irw
df = irw.fetch("pinho_2026_behavioral_intention")
@misc{https://doi.org/10.6084/m9.figshare.33296244.v1,
doi = {10.6084/M9.FIGSHARE.33296244.V1},
url = {https://figshare.com/articles/dataset/Generative_AI_Use_AI_Literacy_and_Self-Efficacy_among_Portuguese_Higher-Education_Students_N_204_/33296244/1},
author = {Pinho, claudia Sofia Borges},
keywords = {Information systems education, Human-centred computing not elsewhere classified},
title = {Generative AI Use, AI Literacy and Self-Efficacy among Portuguese Higher-Education Students (N = 204)},
publisher = {figshare},
year = {2026},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v607 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v10.0 |
| Redivis dataset DOI | 10.57761/45xf-md22 |
| Manifest pin for this IRW version | v9.0 |
| Metadata source | irw_meta v47.0 |