Mapping the Sustainable Human-Resource Challenges in Southeast Asia’s FinTech Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The significance of human resources (HRs) has increased with the increasing awareness of sustainability issues and corporate social responsibility. However, the rapidly emerging financial technology (FinTech) sector still presents an HR challenge. Southeast Asia, which accounts for the highest adoption rate of mobile banking, has set new records regarding the number of transactions, as well as funding amount, in recent years. Moreover, borderless financial cooperation, coupled with in-demand tech talents, will rapidly boost the development of the region. Thus, this study explored the new opportunities as well as challenges of a new business model, FinTech, in Southeast Asia’s banking and enterprise sector in the post-COVID-19 era. It also examined how organizations can achieve sustainable development via the interaction of the new operating model with existing ones by developing relevant strategies in the context of the “new normal” working condition. By reviewing the literature on HR management (HRM), we proposed how banking and FinTech companies could supply tech talent with the relevant experience or engage in training projects before recruiting. Additionally, since organizations desire sustainability-minded employees, they offer flexible working arrangements and well-established reward policies that can create remote work performance and retention rates. Being committed to upskilling and reskilling global talent by offering talent mobility opportunities across the organization, as well as by fully embracing the creation of value for cross-cultural talent, companies can support their employees’ long-term career goals and maintain competitive strength. Finally, organizations must focus more on flexible adjustments and cross-domain communication for global talent. Forming strategic alliances with FinTech companies would be an alternative conduit that can ensure that regional laws comply with the local culture and national law, for bias and conflict reduction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it