7,378 rated or chosen profiles from 315 respondents, in up to 12 tasks of 2 profiles varying on 3 attributes.
One of 6 tables from the same source; this one is the employment table. The others are listed under Related tables.
| Description | MTurk (TurkPrime) participants in 2018 (n = 315) evaluated 12 fixed pairs of versions of an employment algorithm, varying outcome favourability for the respondent, data sharing (none or one of two partners) and data protection; outcomes: choice of version A, B or neither, and 0-100 moral acceptability of each version. One of six technology tables from the same study; each respondent did two technologies. |
|---|---|
| Reference | Kodapanakkal, R. I., Brandt, M. J., Kogler, C., & van Beest, I. (2020). Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach. Computers in Human Behavior, 108, 106303. |
| DOI | 10.1016/j.chb.2020.106303 |
| Licence | CC BY |
| Source data | https://doi.org/10.34894/YUCT6Q |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 315 |
|---|---|
| Rows (one per respondent, task and profile) | 7,378 |
| Tasks per respondent (most) | 12 |
| Profiles per task | 2 |
| Attributes | 3 |
| Tasks with no profile chosen | 930 |
| Topic | technology ai privacy |
|---|---|
| A profile represents | product service |
| Country | unknown |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): fixed design: the same 12 A/B pairs (codingamce.csv) for every respondent; each level appears equally often across the 24 profiles (favourability 12/12, sharing 8/8/8, protection 12/12); not all level combinations are paired |
| Task order | unknown |
| Profile position | recorded in the deposit |
Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.
Outcomes:
| choice | If you have to make a choice between these versions of the technology, which one would you choose? [choice, opt-out allowed] |
|---|---|
| rating | On a scale of 0 to 100, how would you morally evaluate Technology version A? (where 0 is morally unacceptable and 100 is morally acceptable) [rating, 0 (Morally unacceptable) to 100 (Morally acceptable)] |
attr_data_protectionattr_data_sharingattr_outcomechoiceidprofileratingtasktrial_framing
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("kodapanakkal_2020_employment", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("kodapanakkal_2020_employment", source="conj")
@article{Kodapanakkal_2020, title={Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach}, volume={108}, ISSN={0747-5632}, url={http://dx.doi.org/10.1016/j.chb.2020.106303}, DOI={10.1016/j.chb.2020.106303}, journal={Computers in Human Behavior}, publisher={Elsevier BV}, author={Kodapanakkal, Rabia I. and Brandt, Mark J. and Kogler, Christoph and van Beest, Ilja}, year={2020}, month=July, pages={106303} }
| 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 |