1,601 responses from 352 respondents to 5 items.
One of 8 tables from the same source; this one is the policy_environment table. The others are listed under Related tables.
| Description | Policy environment scale (5 items 7-pt Likert) rated by managers/employees at Chinese time-honored (heritage) enterprises |
|---|---|
| Reference | Li X, et al (2025) Breaking the deadlock: A study on the pathway and effects of reshaping the sustainable marketing capability of Chinese time-honored brands. PLoS ONE 20(6): e0326329 |
| DOI | 10.1371/journal.pone.0326329 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0326329 |
| Responses | 1,601 |
|---|---|
| Respondents | 352 |
| Items | 5 |
| Response categories | 5 |
| Responses per respondent | 4.548 |
| Responses per item | 320.200 |
| Density | 0.910 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| 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 | Policy Environment (PE) scale |
|---|---|
| Mean words per item | 11.400 |
| Mean characters per item | 85.400 |
| Mean characters per response | 13 |
| Flesch-Kincaid grade level | 15.561 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, li_2025_marketing_capability.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_firm_size | Covariate | Size |
cov_firm_type | Covariate | Type |
cov_gender | Covariate | Gender |
cov_industry | Covariate | Industry |
cov_location | Covariate | Located |
cov_position | Covariate | Position |
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("li_2025_policy_environment")
# Python
pip install irw
import irw
df = irw.fetch("li_2025_policy_environment")
@article{Li_2025, title={Breaking the deadlock: A study on the pathway and effects of reshaping the sustainable marketing capability of Chinese time-honored brands}, volume={20}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0326329}, DOI={10.1371/journal.pone.0326329}, number={6}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Li, Xiangyu and Inthurit, Danaikrit and Wu, MaoChun and Qiu, Linjun}, editor={Herrador, Manuel}, year={2025}, month=June, pages={e0326329} }
| IRW version | v566 |
|---|---|
| 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 v43.0 |