Purchase Intention Dataset Based on AI Social Commerce, Influencer Marketing and FoMO
Description
This dataset contains responses collected for a study examining the influence of Artificial Intelligence (AI) Social commerce, influencer marketing, and Fear of Missing Out (FoMO) on fashion purchase intention. The data were gathered from [insert number] participants using a structured online questionnaire with Likert scale items. Two analytical tools were used: SmartPLS 4.0 for Partial Least Squares Structural Equation Modeling (PLS-SEM), and RapidMiner for decision tree classification. Variables include AI-driven commerce factors, influencer credibility, FoMO dimensions (emotional response, peer influence, etc.), and consumer purchase intention. This dataset can be valuable for researchers studying digital marketing, AI in Social commerce, Marketing strategy like Influencer marketing, consumer behavior, and psychological factors like FoMO.
Files
Steps to reproduce
1. Clean the raw data using Microsoft Excel or RapidMiner. 2. Load the data into SmartPLS 4.0 to perform PLS-SEM analysis: - Run outer loading for indicator reliability. - Test construct validity using HTMT and Fornell-Larcker. - Analyze path coefficients, T-statistics, and P-values. 3. Use RapidMiner to run a decision tree classification to identify which variables most influence purchase intention. 4. Interpret results to evaluate the role of AI commerce, influencer marketing, and FoMO. Software required: - SmartPLS 4.0 - RapidMiner Studio - Excel or CSV reader
Institutions
- Bina Nusantara University