9,870 rated or chosen profiles from 329 respondents, in up to 10 tasks of 3 profiles varying on 4 attributes.

About this table

Description	Dutch residential electricity users chose among three electricity bundles that varied by supplier (conventional or local P2P market), energy type, selling price and monthly cost. 329 respondents, 10 choice tasks, forced choice.
Reference	Dukovska, I. (2022). User preferences for local P2P electricity markets - Dataset [Data set]. 4TU.ResearchData.
Licence	CC BY 4.0
Source data	https://doi.org/10.4121/20324832

Notes

Conjoint table (irw_conjoint_2). Choice of one of three electricity bundles (4 attributes) in 10 tasks; forced choice, no none option. Attribute levels are the authors' English translation of the Dutch survey, with code order taken from the Qualtrics level list; the design file and responses were joined on the Qualtrics version number. 329 respondents (12 did not finish the later background questions but answered all tasks); gender, birth year, education (Dutch), province kept.

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

Size and shape

Respondents	329
Rows (one per respondent, task and profile)	9,870
Tasks per respondent (most)	10
Profiles per task	3
Attributes	4
Tasks with no profile chosen	0

Design

Topic	climate energy environment
A profile represents	product service
Country	NL
Language shown	nl
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	If you had to choose between the following electricity bundles, which on would you choose? (translated) [choice, forced]

Columns

attr_energy_typeattr_monthly_costattr_selling_priceattr_supplierchoicecov_birth_yearcov_educationcov_gendercov_provinceidprofiletask

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 needed Browse on Redivisexplore and query Croissant 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("dukovska_2022_p2p_electricity", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("dukovska_2022_p2p_electricity", source="conj")

How to cite

@misc{https://doi.org/10.4121/20324832,
  doi = {10.4121/20324832},
  url = {https://data.4tu.nl/articles/_/20324832},
  author = {Dukovska, Irena},
  keywords = {Electrical and Electronic Engineering, FOS: Electrical engineering, electronic engineering, information engineering, User Preferences, Choice-based Conjoint, Discrete Choice Experiment, P2P electricity trading, Time: 2021},
  title = {User preferences for local P2P electricity markets - Dataset},
  publisher = {4TU.ResearchData},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

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