When the digital commons freeze: how perceived AI information misuse drives visual creators' defensive withdrawal through resource erosion — data and analysis code
Authors/Creators
- 1. Faculty of Humanities and Social Sciences, City University of Macau
Description
Survey data and analysis code for a study of how visual creators respond to
perceived misuse of their work as training data for generative AI.
Cross-sectional survey of 513 visual creators publishing original work on
public platforms, collected in September 2026. Nine constructs measured with
33 seven-point Likert items. Analysed through PLS-SEM, an artificial neural
network and necessary condition analysis.
Files: full sample (N = 550, with exclusion flag), analysis sample (N = 513),
latent variable scores, codebook with English and Chinese item wording and
scale sources, and R scripts for the neural network and necessary condition
analysis. See README.md for details.
The files contain no direct or indirect identifiers.
Files
Restricted
The record is publicly accessible, but files are restricted. Log in to check if you have access.