7,454 rated or chosen profiles from 314 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 investigations table. The others are listed under Related tables.
| Description | MTurk (TurkPrime) participants in 2018 (n = 314) evaluated 12 fixed pairs of versions of an aerial camera surveillance technology for solving crimes, 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 | 314 |
|---|---|
| Rows (one per respondent, task and profile) | 7,454 |
| Tasks per respondent (most) | 12 |
| Profiles per task | 2 |
| Attributes | 3 |
| Tasks with no profile chosen | 1,025 |
| 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_investigations", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("kodapanakkal_2020_investigations", 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 |