1,760 responses from 352 respondents to 5 items.
One of 8 tables from the same source; this one is the marketing_operation table. The others are listed under Related tables.
| Description | Marketing operation 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 | 1,760 |
|---|---|
| Respondents | 352 |
| Items | 5 |
| Response categories | 5 |
| Responses per respondent | 5 |
| Responses per item | 352 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.
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.
Respondent uniqueness. No respondent in this table is unique on the 2 covariates above: every one of them shares their combination with someone else. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.
Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("irw")
library(irw)
df <- irw_fetch("li_2025_marketing_operation")
# Python
pip install irw
import irw
df = irw.fetch("li_2025_marketing_operation")
Or in Stata, which reads the same no-account CSV as the button above:
* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")
irw_fetch li_2025_marketing_operation, clear
@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 | v674 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v16.0 |
| Redivis dataset DOI | 10.57761/3rv0-z494 |
| Manifest pin for this IRW version | v16.0 |
| Metadata source | irw_meta v59.0 |