78,420 rated or chosen profiles from 6,535 respondents, in up to 6 tasks of 2 profiles varying on 2 attributes.
| Description | Swiss 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. |
|---|---|
| Reference | Gomm et al. (2026). Comparing and combining theory of planned behavior and dragons of inaction to explain pro-environmental consumption behavior. Sustainable Production and Consumption. |
| DOI | 10.1016/j.spc.2026.04.008 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.5281/zenodo.20008483 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 6,535 |
|---|---|
| Rows (one per respondent, task and profile) | 78,420 |
| Tasks per respondent (most) | 6 |
| Profiles per task | 2 |
| Attributes | 2 |
| Tasks with no profile chosen | 0 |
| Topic | consumer products |
|---|---|
| A profile represents | product service |
| Country | CH |
| Language shown | unknown |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| choice | Which of the two products would you buy? (paraphrase) [choice, forced] |
|---|---|
| rating | How 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)] |
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.
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")
@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} }
| 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 |