1,850 responses from 185 respondents to 10 items.
One of 3 tables from the same source; this one measures Job burnout. The others are listed under Related tables.
| Description | Job burnout, 10 items (KE1-3, MPP1-4, D1-3), 1-5, 185 employed adults in Jakarta, Indonesia; cov_gender, cov_age_band, cov_tenure, cov_education, cov_job_level, cov_job_field, cov_industry, cov_cyberloafing_time. |
|---|---|
| Reference | Harlianto, J., & Kiolol, N. G. P. (2025). Job Stress, Cyberloafing, and Burnout [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16890070 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/16890070 |
| Responses | 1,850 |
|---|---|
| Respondents | 185 |
| Items | 10 |
| Response categories | 5 |
| Responses per respondent | 10 |
| Responses per item | 185 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| construct type | Affective/mental health |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ind |
| construct name | Job burnout |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, harlianto_2025_cyberloafing.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). | No. |
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 | Umur |
cov_cyberloafing_time | Covariate | made in the build script |
cov_education | Covariate | Pendidikan terakhir |
cov_gender | Covariate | Jenis Kelamin |
cov_industry | Covariate | Jenis industri dari tempat bekerja Anda saat ini |
cov_job_field | Covariate | Bidang Pekerjaan |
cov_job_level | Covariate | Tingkat jabatan (setara dengan jabatan) |
cov_tenure | Covariate | Lama Bekerja |
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("harlianto_2025_burnout")
# Python
pip install irw
import irw
df = irw.fetch("harlianto_2025_burnout")
@misc{https://doi.org/10.5281/zenodo.16890070,
doi = {10.5281/ZENODO.16890070},
url = {https://zenodo.org/doi/10.5281/zenodo.16890070},
author = {Harlianto, Jefta and Kiolol, Nadya Gwynni Pankrasius},
keywords = {human resources},
language = {id},
title = {Job Stress, Cyberloafing, and Burnout},
publisher = {Zenodo},
year = {2025},
copyright = {Creative Commons Attribution 4.0 International}
}
| 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 |