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Data · dataset · 2026

Dataset for Research on ICH Clothing Purchase Intention

Listed in Teesside University Research Data Repository

Description

This dataset supports the study “Cultural Value, Social Identity, and Purchase Intention of Intangible Cultural Heritage (ICH) Clothing: The Moderating Role of Willingness to Pay a Premium.” The study examines whether the cultural value of ICH clothing is associated with three dimensions of social identity—cognitive, affective, and evaluative identity—and whether these dimensions are related to consumers’ purchase intention.

It also tests whether willingness to pay a premium strengthens the relationships between affective and evaluative identity and purchase intention. The data were collected through a structured questionnaire survey in China. A total of 463 valid responses were retained.

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The questionnaire measured cultural value, cognitive social identity, affective social identity, evaluative social identity, purchase intention, willingness to pay a premium, and selected demographic characteristics. Variables are numerically coded, and higher scores generally indicate stronger agreement with the corresponding statements. Structural equation modeling was used to test the proposed relationships, while moderation analysis examined the role of willingness to pay a premium.

The results show that cultural value is positively associated with cognitive and evaluative social identity, but not significantly associated with affective social identity. All three social identity dimensions are positively associated with purchase intention. Willingness to pay a premium also strengthens the relationships between affective identity and purchase intention and between evaluative identity and purchase intention.

The dataset can be used to reproduce the reported analyses or support further research on ICH clothing, cultural consumption, social identity, consumer behavior, and purchase intention. Because the data are cross-sectional and self-reported, the results should be interpreted as statistical associations rather than causal relationships.

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