Employee Engagement and Green Finance: An Analysis of Indonesian Banking Sustainability Reports
Bibliographic record
Abstract
Green finance has emerged as a critical driver of sustainable development for the banking industry. Engaging employees is essential for the successful implementation of green finance initiatives. This study aims to examine the employee engagement strategies of leading Indonesian banks and compare them with non-banking financial institutions. By analyzing sustainability reports and ESG risk ratings, this study identifies key employee engagement practices in the green finance context, compares them with those of non-banking institutions, and explores the link between green finance, employee engagement, and ESG risk ratings. Drawing on stakeholder theory and an ethical sustainability governance framework, this content analysis study reveals that Indonesian banks primarily focus on training, labor rights, and diversity as key employee engagement practices. While these practices are consistent across materiality, strategy, and performance, they may not fully capture the nuances of employee engagement in the context of green finance. When compared to non-banking institutions, Indonesian banks exhibit a stronger focus on all employee engagement parameters. However, a potential link between green finance, employee engagement, and ESG risk ratings is not evident. The current ESG rating methodologies may prioritize the quantity and quality of sustainability reporting over the actual implementation of impactful sustainable practices, particularly in employee engagement practices and green finance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".