13,824 rated or chosen profiles from 432 respondents, in up to 8 tasks of 4 profiles varying on 5 attributes.
| Description | Serbian university-student participants (n = 432) made 8 choices among 4 hypothetical job candidates varying in an AI employability score (7-91%), required education, gender, age and hobby, then said whether they would actually advance the chosen candidate (dual response). |
|---|---|
| Reference | Petrović, N., Kuzmanović, M., Anđelković Labrović, J., Kovačević, I., & Llorens, A. (2026). Data for: How much weight does an AI score carry in hiring? Qualification conflict and evaluator heterogeneity in a choice-based conjoint experiment [Data set]. Zenodo. |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.5281/zenodo.20703547 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 432 |
|---|---|
| Rows (one per respondent, task and profile) | 13,824 |
| Tasks per respondent (most) | 8 |
| Profiles per task | 4 |
| Attributes | 5 |
| Tasks with no profile chosen | 0 |
| Topic | labour hiring, technology ai privacy |
|---|---|
| A profile represents | person |
| Country | RS |
| Language shown | sr |
| Language of stored levels | sr |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| choice | Choice among four candidate profiles (forced first stage) (paraphrase) [choice, forced] |
|---|---|
| choice_commit | Dual-response choice: the picked candidate if the participant would actually advance them, otherwise none (paraphrase of the second-stage hiring-commitment question) [choice, opt-out allowed] |
attr_ageattr_ai_scoreattr_educationattr_genderattr_hobbychoicechoice_commitcov_agecov_ai_in_hiringcov_educationcov_employmentcov_field_of_studycov_gendercov_hiring_experiencecov_hobbiescov_positioncov_q13_ai_familiaritycov_q14_ai_usagecov_q15_ai_decision_makingcov_q16_ai_recommendation_satisfactioncov_q17_ai_advice_verificationcov_q18_ai_tools_understandingcov_q20_experience_ratingcov_q21_ai_candidate_assessmentcov_q22_ai_assessment_objectivitycov_q23_confidence_assessmentcov_q24_ai_job_candidate_assessmentcov_wave2idprofiletask
The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.
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("petrovic_2026_ai_hiring_score", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("petrovic_2026_ai_hiring_score", source="conj")
@misc{https://doi.org/10.5281/zenodo.20703547,
doi = {10.5281/ZENODO.20703547},
url = {https://zenodo.org/doi/10.5281/zenodo.20703547},
author = {Petrovic, Nikola and Kuzmanovic, Marija and Andjelkovic Labrovic, Jelena and Kovačević, Ivana and Llorens, Ariadna},
title = {Data for: How Much Weight Does an AI Score Carry in Hiring? Qualification Conflict and Evaluator Heterogeneity in a Choice-Based Conjoint Experiment},
publisher = {Zenodo},
year = {2026},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint v4.2 |
| Redivis dataset DOI | 10.57761/qkye-q530 |
| Manifest pin for this IRW version | v4.2 |
| Metadata source | irw_meta v44.0 |