8,300 responses from 415 respondents to 20 items.
One of 3 tables from the same source; this one measures Digital banking motivation, satisfaction, confirmation, usefulness, continuance intention. The others are listed under Related tables.
| Description | Motivation MOT1-4, satisfaction SAT1-4, confirmation CON1-4, usefulness USE1-4 and continuance intention CI1-4; 20 items (1-5); N = 415 Indonesian app users; no covariates. |
|---|---|
| Reference | Heryanda, O., Wafa, R., & Azhari, S. (2025). Research Questionnaire [digital banking gamification survey data]. Mendeley Data. https://doi.org/10.17632/mkd998xj9j |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/mkd998xj9j |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 8,300 |
|---|---|
| Respondents | 415 |
| Items | 20 |
| Response categories | 5 |
| Responses per respondent | 20 |
| Responses per item | 415 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ind |
| construct name | Digital banking motivation, satisfaction, confirmation, usefulness, continuance intention |
This codebook is reconstructed by the IRW from the IRW data standard. 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
What item codes and covariate values stand for is in the codebook released with the source data.
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("heryanda_2025_usage_outcomes")
# Python
pip install irw
import irw
df = irw.fetch("heryanda_2025_usage_outcomes")
@misc{https://doi.org/10.17632/mkd998xj9j,
doi = {10.17632/MKD998XJ9J},
url = {https://data.mendeley.com/datasets/mkd998xj9j},
author = {Heryanda, Oktavian and Wafa, Rachel and Azhari, Shofi},
keywords = {Banking},
title = {Research Questionnaire},
publisher = {Mendeley Data},
year = {2025}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_7 v1.1 |
| Redivis dataset DOI | 10.57761/7pd3-sh31 |
| Manifest pin for this IRW version | v1.0 |
| Metadata source | irw_meta v44.0 |