From risk-taking to innovation: Managerial and policy insights on risk management in dual-purpose ventures
Bibliographic record
Abstract
This article extends the recent empirical findings of Im and Sun in 2025 with a practice- and policy-oriented focus. It investigates how microfinance institutions (MFIs) respond to financial underperformance, revealing that MFIs, particularly nonprofit ones and those operating in inefficient institutional environments, often engage in excessive risk-taking behaviors during their problemistic search processes. While such behaviors may offer short-term relief, they can also lead to long-term financial instability and threaten MFIs’ social missions. Therefore, when confronting financial challenges, their problemistic search should focus on identifying innovative solutions that enhance financial sustainability without compromising social goals, rather than resorting to excessive risk-taking. This article offers actionable recommendations for MFI practitioners and policymakers to support MFIs in prioritizing innovation over excessive risk-taking. In doing so, it translates academic insights into practical strategies for dual-purpose ventures.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".