brueckmann_2023_charging_stations

28,310 rated or chosen profiles from 2,831 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.

About this table

DescriptionSwiss 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.
ReferenceBrückmann, G., & Bernauer, T. (2023). An experimental analysis of consumer preferences towards public charging infrastructure. Transportation Research Part D: Transport and Environment, 121, 103626.
DOI10.1016/j.trd.2023.103626
LicenceCC BY 4.0
Source datahttps://doi.org/10.5281/zenodo.18232567

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents2,831
Rows (one per respondent, task and profile)28,310
Tasks per respondent (most)5
Profiles per task2
Attributes5
Tasks with no profile chosen0

Design

Topicclimate energy environment
A profile representsproduct service
CountryCH
Language shownde
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positioninferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choiceWelche Ladestation würden Sie nutzen? (Ladestation A / Ladestation B) (translated: Which charging station would you use?) [choice, forced]

Columns

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.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

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")

How to cite

@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} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint_2 v1.4
Redivis dataset DOI10.57761/hk97-wv30
Manifest pin for this IRW versionv1.4
Metadata sourceirw_meta v44.0