17,666 responses from 310 respondents to 57 items.
| Description | UTAUT2 questionnaire on generative-AI tool use (PE, EE, SI, HM, PV, FC, HT, BU, UT blocks), 57 items, 1-5; N = 310 marketing undergraduates in Peru and Costa Rica; cov_age, cov_gender, cov_university. |
|---|---|
| Reference | Pinedo, L. (2026). Survey on the Use of Generative Artificial Intelligence Tools Among Marketing Undergraduates [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.20400330 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/20400330 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 17,666 |
|---|---|
| Respondents | 310 |
| Items | 57 |
| Response categories | 5 |
| Responses per respondent | 56.987 |
| Responses per item | 309.930 |
| Density | 1.000 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | spa |
| construct name | UTAUT2 acceptance and use of generative AI tools |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, pinedo_2026_genai_utaut2.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 | Covariate | made in the build script |
cov_gender | Covariate | made in the build script |
cov_university | Covariate | made in the build script |
What item codes and covariate values stand for is in the source’s codebook, Database.xlsx.
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("pinedo_2026_genai_utaut2")
# Python
pip install irw
import irw
df = irw.fetch("pinedo_2026_genai_utaut2")
@misc{https://doi.org/10.5281/zenodo.20400330,
doi = {10.5281/ZENODO.20400330},
url = {https://zenodo.org/doi/10.5281/zenodo.20400330},
author = {Pinedo, Lloy},
language = {en},
title = {Survey on the Use of Generative Artificial Intelligence Tools Among Marketing Undergraduates},
publisher = {Zenodo},
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 |