Analytical Dataset: PLS-SEM and Multi-Group Analysis Results for AI Adoption in Nanostores Across the Middle East and Latin America

This dataset supports the study titled “Testing the Micro-Retail Boundary: A Cross-Regional PLS-SEM and MGA Study of AI Adoption in Middle Eastern and Latin American Nanostores Using the Extended TOE Framework.”

The dataset includes both raw and processed data collected from nanostore owners and managers across seven countries in the Middle East (Saudi Arabia, UAE, Israel, Jordan, Morocco, West Bank) and Latin America (Colombia, Honduras).

The study investigates the determinants of Artificial Intelligence (AI) adoption in resource-constrained micro-retail environments using an extended Technology–Organization–Environment (TOE) framework integrated with dynamic capabilities.

The dataset contains:
- Raw survey data (anonymized)
- Measurement model results (reliability, validity, factor loadings)
- Structural model results (path coefficients, R², effect sizes f²)
- Bootstrapping results (significance testing)
- Multi-Group Analysis (MGA) results across regions

All analyses were conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS-based Multi-Group Analysis (PLS-MGA).

Files are organized as follows:
1. Raw dataset (01_Raw_Data.xlsx)
2. Measurement model results (02_Measurement_Model.xlsx)
3. Bootstrapping and structural model results (03_Bootstrapping_Results.xlsx)
4. Multi-Group Analysis results (04_MGA_Results.xlsx)

This dataset is provided to ensure transparency, reproducibility, and to support future research on AI adoption, digital transformation, and micro-retail ecosystems in emerging markets.

All data have been anonymized and comply with ethical research standards.