1,200 rated or chosen profiles from 100 respondents, in up to 12 tasks of 1 profile varying on 3 attributes.
| Description | Indonesian consumers rated 12 fixed tea-drink product cards (flavour, flavour intensity, pack type; a 3x2x2 full factorial, text in Indonesian and English). 100 respondents, 12 single-profile cards, one 1-5 score. |
|---|---|
| Reference | Hariyoko, C., & Murniati, M. (2024). Strategic implications of consumer preferences on product development: A conjoint analysis of the We Need Tea market in Indonesia data [Data set]. Mendeley Data. |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.17632/t3b628s7w4 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 100 |
|---|---|
| Rows (one per respondent, task and profile) | 1,200 |
| Tasks per respondent (most) | 12 |
| Profiles per task | 1 |
| Attributes | 3 |
| Topic | consumer products |
|---|---|
| A profile represents | product service |
| Country | ID |
| Language shown | id |
| Language of stored levels | id |
| 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:
| rating | unknown (no question wording in the deposit); score of each stimulus card, 1-5 [rating, 1 (unknown) to 5 (unknown)] |
|---|
attr_flavourattr_flavour_intensityattr_packcov_purchase_frequencyidprofileratingtask
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("hariyoko_2024_tea_preferences", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("hariyoko_2024_tea_preferences", source="conj")
@misc{https://doi.org/10.17632/t3b628s7w4,
doi = {10.17632/T3B628S7W4},
url = {https://data.mendeley.com/datasets/t3b628s7w4},
author = {Hariyoko, Chelytha and Murniati, Murniati},
keywords = {Questionnaire},
title = {Strategic Implications of Consumer Preferences on Product Development: A Conjoint Analysis of the We Need Tea Market in Indonesia Data},
publisher = {Mendeley Data},
year = {2024}
}
| 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 |