13,500 rated or chosen profiles from 300 respondents, in up to 15 tasks of 3 profiles varying on 5 attributes.

About this table

Description	Online discrete choice experiment among 300 Singapore shark fin consumers: in each of 15 choice sets respondents chose among three shark fin soups described by price, fin size, texture and quality, menu type and source of fin, or none of them.
Reference	Choy, C., Booth, H., & Verissimo, D. (2024). Understanding consumers to inform market interventions for Singapore's shark fin trade. People and Nature, 6, 733-748.
DOI	10.1002/pan3.10590
Licence	CC BY 4.0
Source data	https://doi.org/10.5281/zenodo.10421161

Notes

Conjoint table (irw_conjoint_2). Choice of one of three shark fin soups (alternatives a, b, c) or none of them, in 15 sets per respondent (Bayesian D-efficient design); a fixed "No shark fin" option is the opt-out and has no profile row. Level text is the article's Table 1, mapped from the deposit's numeric codes by code order and verified by reproducing the article's multinomial logit; task is the design set number, since question order was randomized and not stored. Age, education and income stay as codes (no codebook).

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	300
Rows (one per respondent, task and profile)	13,500
Tasks per respondent (most)	15
Profiles per task	3
Attributes	5
Tasks with no profile chosen	498

Design

Topic	consumer products, climate energy environment
A profile represents	product service
Country	SG
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	inferred from row order
Profile position	recorded in the deposit

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

Outcomes:

choice	Which shark fin soup would you purchase (three shark fin soups or none of them) (paraphrase) [choice, opt-out allowed]

Columns

attr_fin_sizeattr_fin_sourceattr_menu_typeattr_priceattr_texture_qualitychoicecov_age_codecov_education_codecov_gendercov_income_codeidprofiletasktrial_none_chosen

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("choy_2024_shark_fin", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("choy_2024_shark_fin", source="conj")

How to cite

@article{Choy_2024, title={Understanding consumers to inform market interventions for Singapore’s shark fin trade}, volume={6}, ISSN={2575-8314}, url={http://dx.doi.org/10.1002/pan3.10590}, DOI={10.1002/pan3.10590}, number={2}, journal={People and Nature}, publisher={Wiley}, author={Choy, Christina and Booth, Hollie and Veríssimo, Diogo}, year={2024}, month=Jan, pages={733–748} }

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