2,610 responses from 435 respondents to 6 items.
One of 6 tables from the same source; this one is the ewom table. The others are listed under Related tables.
| Description | 6-item eWOM (electronic word-of-mouth) intention scale (1-5 Likert), same sports-tourism social-media sample as li_2025_socmedia_usefulness, n=435 |
|---|---|
| Reference | Li X, et al (2025). The impact of social media on university students' revisit intention in sports tourism: A hybrid method based on SEM and ANN. PLOS ONE. |
| DOI | 10.1371/journal.pone.0321999 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321999 |
| Responses | 2,610 |
|---|---|
| Respondents | 435 |
| Items | 6 |
| Response categories | 5 |
| Responses per respondent | 6 |
| Responses per item | 435 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Electronic word-of-mouth scale (Xu et al., 2024) |
|---|---|
| Mean words per item | 16.333 |
| Mean characters per item | 119.500 |
| Mean characters per response | 3.200 |
| Flesch-Kincaid grade level | 13.828 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, li_2025_sports_tourism_socialmedia.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). | index |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_completion_time_s | Covariate | totalseconds |
cov_gender | Covariate 1 = 男 · 2 = 女 | Gender |
cov_grade | Covariate 1 = 一年级 · 2 = 二年级 · 3 = 三年级 · 4 = 四年级 | Grade |
cov_school_level | Covariate 1 = 985/211高校 · 2 = 普通本科 · 3 = 专科 | School_level |
cov_source_region | Covariate 1 = 城市 · 2 = 乡镇 · 3 = 农村 | Soure |
cov_subject | Covariate 1 = 体育类 · 2 = 非体育类 | Subject |
Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled 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("li_2025_socmedia_ewom")
# Python
pip install irw
import irw
df = irw.fetch("li_2025_socmedia_ewom")
@article{Li_2025, title={The impact of social media on university students’ revisit intention in sports tourism: A hybrid method based on SEM and ANN}, volume={20}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0321999}, DOI={10.1371/journal.pone.0321999}, number={4}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Li, Jinrong and Li, Fang and Zhou, Xiaomin}, editor={Jayasingh, Sudarsan}, year={2025}, month=Apr, pages={e0321999} }
| 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 |