8,277 responses from 267 respondents to 31 items.
| Description | Luxury-purchase-intention battery, online consumers, 31 items, N=267 |
|---|---|
| Reference | Majeed, M. U., et al. (2024). Determining online consumer's luxury purchase intention: The influence of antecedent factors and the moderating role of brand awareness, perceived risk, and web atmospherics. PLOS ONE. |
| DOI | 10.1371/journal.pone.0295514 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295514 |
| Responses | 8,277 |
|---|---|
| Respondents | 267 |
| Items | 31 |
| Response categories | 5 |
| Responses per respondent | 31 |
| Responses per item | 267 |
| Density | 1 |
| Longitudinal | FALSE |
| measurement tool | Survey/questionnaire |
|---|---|
| item format | Likert Scale/selected response |
| primary language(s) | urd |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Online Luxury Purchase Intention Questionnaire (Majeed et al., 2024) |
|---|---|
| Mean words per item | 11.677 |
| Mean characters per item | 69.129 |
| Mean characters per response | 10.200 |
| Flesch-Kincaid grade level | 9.207 |
iditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("majeed_2024_luxury_purchase")
# Python
pip install irw
import irw
df = irw.fetch("majeed_2024_luxury_purchase")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v7.0 |
| Redivis dataset DOI | 10.57761/pqqn-pm43 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |