Automated Author ProfileZahra, Syakira
Binus University
Zahra, Syakira
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.8 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The dataset was developed to examine the impact of artificial intelligence adoption on the productivity of young workers aged 21 to 30. The analysis includes models with six variables: intended use of artificial intelligence, frequency of artificial intelligence usage, level of artificial intelligence understanding, quality of work results, time efficiency, and young workers’ productivity. Data were collected using a structured questionnaire administered to 464 respondents with work experience or current employment in DKI Jakarta between October 8 and 11, 2025. Structural Equation Modeling (SEM) was employed to analyze the relationships among the variables and to test the hypotheses. The analytical approach in this study includes model specification, assessment of construct validity and reliability, Heterotrait-Monotrait ratio (HTMT), R Square, Standardized Root Mean Square Residual (SRMR), F Square, and path analysis.
Authors
- Zahra, Syakira ;
- Atmojo, Robertus
The dataset was developed to examine the impact of artificial intelligence adoption on the productivity of young workers aged 21 to 30. The analysis includes models with six variables: intended use of artificial intelligence, frequency of artificial intelligence usage, level of artificial intelligence understanding, quality of work results, time efficiency, and young workers’ productivity. Data were collected using a structured questionnaire administered to 464 respondents with work experience or current employment in DKI Jakarta between October 8 and 11, 2025. Structural Equation Modeling (SEM) was employed to analyze the relationships among the variables and to test the hypotheses. The analytical approach in this study includes model specification, assessment of construct validity and reliability, Heterotrait-Monotrait ratio (HTMT), R Square, Standardized Root Mean Square Residual (SRMR), F Square, and path analysis.
Authors
- Zahra, Syakira ;
- Atmojo, Robertus
This table presents the operational variables and corresponding statements designed for inclusion in the questionnaire to collect data for the study titled “The Impact of Artificial Intelligence Adoption on the Productivity of Young Workers Aged 21 to 30.”
Authors
- Zahra, Syakira ;
- Atmojo, Robertus
This table presents the operational variables and corresponding statements designed for inclusion in the questionnaire to collect data for the study titled “The Impact of Artificial Intelligence Adoption on the Productivity of Young Workers Aged 21 to 30.”
Authors
- Zahra, Syakira ;
- Atmojo, Robertus