Digital Heritage User Engagement Survey Data with Psychological Modeling Results

A research dataset from a study of 348 participants examining dual-process mechanisms in digital heritage environments. The data was collected by Cun Shang and published on figshare in April 2026, integrating text mining of user feedback with structured psychological modeling using PLS-SEM. It explores the dissociation between sensory arousal and cognitive identification in digital heritage appropriation.

Use Cases

• Modeling the relationship between technical affordances and hedonic engagement based on the described stimulus-organism-response framework.
• Analyzing the distinct predictive power of narrative quality on cultural identity versus hedonic engagement.
• Investigating the lexical landscape of user feedback for sensory and reflective orientations based on the text mining results.
• Testing cognitive resource competition theories in digital environments using the provided psychological and semantic data.

Strengths

• Includes data from a structured study with 348 participants (N=348).
• Provides specific statistical results, such as a beta coefficient of 0.376 for technical affordances predicting hedonic engagement.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 174.4 KB, indicating limited scope.

Provenance

Source

figshare, author Cun Shang.

Collection Method

Multi-factor analytical design integrating unstructured semantic analysis (text mining) with structured psychological modeling (PLS-SEM) of survey data.

Time Range

Study data collection date is not specified.

Freshness

Last updated 2026-04-10 05:58:28.

Geography

Geographic scope of the participant sample is not specified.