Dataset on Predictors of Consumers' Augmented Reality Technology Adoption in the Vietnamese Fashion Sector

Description

The data was collected through a survey questionnaire administered to 423 Vietnamese participants recruited randomly based on their relevance to identify factors influencing consumer adoption of augmented reality technology for online fashion shopping in Vietnam. The questionnaire contained 33 measurement items for 9 variables related to intentions and attitudes towards augmented reality technology usage. Responses were screened for completeness and validity. The resulting dataset was analyzed using Partial Least Squares Structural Equation Modeling techniques in SPSS and SmartPLS software packages. Demographic profiles of the sample were explored pertaining to gender, age, and location. The statistical analysis enabled assessment of scale validity and reliability regarding Vietnamese fashion consumers' augmented reality adoption.

Steps to reproduce

Procedure: 1. Develop research model and hypotheses by integrating TAM, Social Identity Theory, and Self-Identity Theory frameworks. Add perceived satisfaction as additional construct. 2. Create bilingual survey questionnaire with 33 measurement items for 9 variables identified in research model. 3. Recruit random sample of 423 Vietnamese participants based on relevance to research topic. 4. Administer online questionnaire and collect responses over 6 weeks from October 13 to November 30, 2022. 5. Screen responses for completeness and validity. Clean data by removing invalid or incomplete entries. 6. Analyze cleaned dataset using Partial Least Squares Structural Equation Modeling in SPSS 24.0 and SmartPLS 4.0 software. 7. Generate demographic profiles of sample regarding gender, age, location. 8. Assess reliability and validity of measurement scales using statistical techniques. 9. Interpret PLS-SEM results to identify factors influencing consumer adoption of augmented reality technology for online fashion shopping in Vietnam. 10.Validate research model and determine implications for future research directions.

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