28,310 rated or chosen profiles from 2,831 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.
| Description | Swiss car holders (n=2,831; 950 with a battery-electric vehicle in the household) chose which of two public charging stations to use, varying waiting time, charging time, energy source, amenities and price; 5 tasks x 2 profiles, forced choice. |
|---|---|
| Reference | Brückmann, G., & Bernauer, T. (2023). An experimental analysis of consumer preferences towards public charging infrastructure. Transportation Research Part D: Transport and Environment, 121, 103626. |
| DOI | 10.1016/j.trd.2023.103626 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.5281/zenodo.18232567 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,831 |
|---|---|
| Rows (one per respondent, task and profile) | 28,310 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 2 |
| Attributes | 5 |
| Tasks with no profile chosen | 0 |
| Topic | climate energy environment |
|---|---|
| A profile represents | product service |
| Country | CH |
| Language shown | de |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| 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:
| choice | Welche Ladestation würden Sie nutzen? (Ladestation A / Ladestation B) (translated: Which charging station would you use?) [choice, forced] |
|---|
attr_amenitiesattr_charging_timeattr_energy_sourceattr_priceattr_queueing_time_minchoicecov_bev_in_householdcov_birth_yearcov_gendercov_household_incomecov_panel_samplecov_political_left_rightidprofiletask
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("brueckmann_2023_charging_stations", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("brueckmann_2023_charging_stations", source="conj")
@article{Br_ckmann_2023, title={An experimental analysis of consumer preferences towards public charging infrastructure}, volume={116}, ISSN={1361-9209}, url={http://dx.doi.org/10.1016/j.trd.2023.103626}, DOI={10.1016/j.trd.2023.103626}, journal={Transportation Research Part D: Transport and Environment}, publisher={Elsevier BV}, author={Brückmann, Gracia and Bernauer, Thomas}, year={2023}, month=Mar, pages={103626} }
| 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 |