403 rated or chosen profiles from 403 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.
One of 2 tables from the same source; this one is the prolific table. The others are listed under Related tables.
| Description | Prolific convenience-sample respondents (n = 403, about 100 per cell) read one energy-app vignette randomized by app popularity (popular or unpopular) and whether most or no other providers analyze and sell user data, and rated others' approval, expected invasion, provider trust and willingness to use. |
|---|---|
| Reference | Horne, C., & Przepiorka, W. (2021). Technology use and norm change in online privacy: Experimental evidence from vignette studies. Information, Communication & Society, 24(9), 1212-1228. |
| DOI | 10.1080/1369118X.2019.1684542 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/SBYXYT |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 403 |
|---|---|
| Rows (one per respondent, task and profile) | 403 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 2 |
| Topic | technology ai privacy |
|---|---|
| A profile represents | product service |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Task order | inferred from row order |
| Profile position | inferred from row order |
Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| rating_others_approve | How much do you think most people would approve of the app provider analyzing and selling users' personal information to other companies? [rating, 1 (strongly disapprove) to 10 (strongly approve)] |
|---|---|
| rating_provider_trust | How trustworthy do you think the app provider is? [rating, 1 (not at all) to 10 (very much)] |
| rating_provider_will_invade | If you download the app, how likely is it that this app provider will analyze and sell your personal information? [rating, 1 (very unlikely) to 10 (very likely)] |
| rating_willing_to_use | How willing are you to download and use this app? [rating, 1 (not at all) to 10 (very)] |
attr_app_popularityattr_other_providerscov_agecov_educationcov_gendercov_race_whiteidprofilerating_others_approverating_provider_trustrating_provider_will_invaderating_willing_to_usetask
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("horne_2021_privacy_norms_prolific", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("horne_2021_privacy_norms_prolific", source="conj")
@article{Horne_2019, title={Technology use and norm change in online privacy: experimental evidence from vignette studies}, volume={24}, ISSN={1468-4462}, url={http://dx.doi.org/10.1080/1369118X.2019.1684542}, DOI={10.1080/1369118x.2019.1684542}, number={9}, journal={Information, Communication & Society}, publisher={Informa UK Limited}, author={Horne, Christine and Przepiorka, Wojtek}, year={2019}, month=Nov, pages={1212–1228} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint_2 v1.4 |
| Redivis dataset DOI | 10.57761/hk97-wv30 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |