gomm_2026_sustainable_products

78,420 rated or chosen profiles from 6,535 respondents, in up to 6 tasks of 2 profiles varying on 2 attributes.

About this table

DescriptionSwiss Environmental Panel members (wave 14, n = 6,535) compared two versions of the same consumer product (tomatoes, chocolate, laptop, smartphone, jacket or shirt) varying in price and environmental impact, 6 tasks; forced choice plus a 1-7 likelihood rating of each version.
ReferenceGomm et al. (2026). Comparing and combining theory of planned behavior and dragons of inaction to explain pro-environmental consumption behavior. Sustainable Production and Consumption.
DOI10.1016/j.spc.2026.04.008
LicenceCC BY 4.0
Source datahttps://doi.org/10.5281/zenodo.20008483

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

Respondents6,535
Rows (one per respondent, task and profile)78,420
Tasks per respondent (most)6
Profiles per task2
Attributes2
Tasks with no profile chosen0

Design

Topicconsumer products
A profile representsproduct service
CountryCH
Language shownunknown
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceWhich of the two products would you buy? (paraphrase) [choice, forced]
ratingHow likely are you to buy this product? (paraphrase; variable label conjoint_c<k>_prob_<j>) [rating, 1 (1 - Very unlikely) to 7 (7 - Very likely)]

Columns

attr_impactattr_pricechoicecov_agecov_age_groupcov_agglomerationcov_educationcov_gendercov_household_incomeidprofileratingtasktrial_producttrial_product_category

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("gomm_2026_sustainable_products", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("gomm_2026_sustainable_products", source="conj")

How to cite

@article{Gomm_2026, title={Comparing and combining theory of planned behavior and dragons of inaction to explain pro-environmental consumption behavior}, volume={66}, ISSN={2352-5509}, url={http://dx.doi.org/10.1016/j.spc.2026.04.008}, DOI={10.1016/j.spc.2026.04.008}, journal={Sustainable Production and Consumption}, publisher={Elsevier BV}, author={Gomm, Sarah and Blumer, Yann and Czellar, Sandor and Haasova, Simona and Kühne, Swen J. and Schubert, Renate and Bernauer, Thomas}, year={2026}, month=July, pages={1–17} }

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