About this table

Description	UTAUT2 mobile-shopping questionnaire, 37 items on a 1-7 scale; itemcov_construct carries the UTAUT2 construct. 14,384 responses from 389 respondents on 37 items.
Reference	Huang, T. (2023). Research questionnaire data [Data set]. figshare. Supporting PLOS ONE 18(12), e0295581. https://doi.org/10.1371/journal.pone.0295581
DOI	10.1371/journal.pone.0295581
Licence	CC BY 4.0
Source data	https://doi.org/10.6084/m9.figshare.24811462

Size and shape

Responses	14,384
Respondents	389
Items	37
Response categories	7
Responses per respondent	36.977
Responses per item	388.757
Density	0.999
Longitudinal	FALSE

Classification

measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	chi

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, huang_2023_utaut_mobile_shopping.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

Column	What it is	Source column
IRW standard columns
`id`	IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).	made in the build script
`item`	IRW standard Item identifier.	made in the build script
`resp`	IRW standard Response value.	made in the build script
Respondent covariates
`cov_age_range`	Covariate	`AgeRange39`
`cov_education`	Covariate	`Education40`
`cov_gender`	Covariate	`gender38`
`cov_mobile_shopping_experience`	Covariate	`MobileShoppingExperience42`
`cov_phone_experience`	Covariate	`PhoneExperience41`
Other columns
`itemcov_construct`	Not documented Covariates invariant to measurement probes (item-level attributes).	made in the build script

What item codes and covariate values stand for is in the codebook released with the source data.

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("huang_2023_utaut_mobile_shopping")

# Python
pip install irw

import irw
df = irw.fetch("huang_2023_utaut_mobile_shopping")

How to cite

@article{Huang_2023, title={Expanding the UTAUT2 framework to determine the drivers of mobile shopping behaviour among older adults}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0295581}, DOI={10.1371/journal.pone.0295581}, number={12}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Huang, Tianyang}, editor={Al-Adwan, Ahmad Samed}, year={2023}, month=Dec, pages={e0295581} }

Version and provenance

IRW version	v549
Redivis dataset	item_response_warehouse_4 v13.0
Redivis dataset DOI	10.57761/w8zx-td92
Manifest pin for this IRW version	v13.0
Metadata source	irw_meta v39.0