Indonesian Digital Society Index
Description
The dataset comprises annual Indonesian Digital Society Index (Indeks Masyarakat Digital Indonesia, IMDI) data for 2022–2025, covering 514 regencies and municipalities across Indonesia. The source data were obtained from the official IMDI portal administered by the Center for Digital Human Resources Ecosystem Development, Human Resources Development Agency, Ministry of Communication and Digital Affairs of the Republic of Indonesia: https://imdi.sdmdigital.id/. IMDI measures regional digital-society development through four pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Employment. The deposited files contain the regional indicators used to examine the sequential relationships among digital infrastructure, digital literacy and skills, digital economic empowerment, and labour-market outcomes.
Files
Steps to reproduce
The data used in this study are publicly available from the official Indonesian Digital Society Index portal. Other researchers may independently download the original IMDI data from this website. 1. The data used in this study are publicly available from the official Indonesian Digital Society Index (Indeks Masyarakat Digital Indonesia, IMDI) portal at https://imdi.sdmdigital.id/. Other researchers may independently download the original IMDI data from this website. 2. Download the IMDI data files for 2022, 2023, 2024, and 2025. The files deposited in this repository are based on the publicly downloadable IMDI data used in the associated study. 3. Import the annual data files into spreadsheet or statistical software and retain observations for all 514 Indonesian regencies and municipalities. 4. Harmonise the names and administrative identifiers of regencies and municipalities across the four years. Check for duplicate observations, missing values, and inconsistencies in variable names or measurement scales. 5. Classify the indicators according to the four dimensions examined in the study: digital infrastructure and ecosystem, digital literacy and skills, digital economic empowerment, and employment or labour-market outcomes. 6. Arrange the data according to the temporal specifications described in the associated article. Use the relevant annual observations or time-separated variables for each model specification. 7. Import the analysis-ready dataset into SmartPLS and specify the four constructs as formative composites using the indicators identified in the associated article. 8. Estimate the PLS-SEM model using the PLS algorithm. Assess collinearity among formative indicators and examine the significance and relevance of the indicator weights. 9. Run bootstrapping with 10,000 resamples to test the structural paths, indirect effects, and mediation relationships. 10. Evaluate the structural model using path coefficients, confidence intervals, significance levels, coefficients of determination (R²), effect sizes, and predictive relevance where applicable. 11. Conduct measurement invariance assessment before performing multi-group comparisons between the regional groups described in the article. 12. Estimate the four robustness specifications reported in the article, including the temporally separated specification, and compare the direction, magnitude, and statistical significance of the results. 13. Interpret the findings using the model specification, variable definitions, and analytical procedures reported in the associated manuscript.