9,953 responses from 1,000 respondents to 10 items.
One of 5 tables from the same source; this one measures AI literacy. The others are listed under Related tables.
| Description | AI literacy, 10 items, 1-5; N = 1,000 Indonesian civil servants; cov_age_group, cov_gender, cov_education, cov_tenure, cov_institution_type, cov_job_type, cov_province. |
|---|---|
| Reference | Ginting, W. O., Nasution, M. A., Lumbanraja, V., & Trimurni, F. (2026). Dataset on Digital Competence, AI Literacy, Generative AI Use, Organizational Support, Public Service Motivation, and Work Performance among Indonesian Civil Servants [Dataset]. Mendeley Data, V1. https://doi.org/10.17632/9b26sb2k4g |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/9b26sb2k4g |
| Responses | 9,953 |
|---|---|
| Respondents | 1,000 |
| Items | 10 |
| Response categories | 5 |
| Responses per respondent | 9.953 |
| Responses per item | 995.300 |
| Density | 0.995 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ind |
| construct name | AI literacy |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, ginting_2026_civil_servants.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_group | Covariate | AGE |
cov_education | Covariate | EDUC |
cov_gender | Covariate | GENDER |
cov_institution_type | Covariate | INST_TYPE |
cov_job_type | Covariate | JOB_TYPE |
cov_province | Covariate | PROVINCE |
cov_tenure | Covariate | TENURE |
What item codes and covariate values stand for is in the source’s codebook, file_downloaded.
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("ginting_2026_ai_literacy")
# Python
pip install irw
import irw
df = irw.fetch("ginting_2026_ai_literacy")
@misc{https://doi.org/10.17632/9b26sb2k4g,
doi = {10.17632/9B26SB2K4G},
url = {https://data.mendeley.com/datasets/9b26sb2k4g},
author = {Ginting, Wiro Oktavius and Nasution, Muhammad Arifin and Lumbanraja, Victor and Trimurni, Februati},
keywords = {Social Sciences, Public Administration},
title = {Dataset on Digital Competence, AI Literacy, Generative AI Use, Organizational Support, Public Service Motivation, and Work Performance among Indonesian Civil Servants},
publisher = {Mendeley Data},
year = {2026}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |