Managerial Myopia Control to Improve Financial Performance in Microfinance Institutions
Bibliographic record
Abstract
Management makes many strategic decisions that have an impact on the performance of the company.Decision-making carried out by management allows for decision-making bias.Our research seeks to gain a further understanding of the managerial phenomenon of myopia, where previous empirical research has been very limited and conceptual.This study aims to analyze the influence of managerial myopia on financial performance in microfinance institutions.This decision-making bias is a concern because of the impact it causes and is often not realized by many managers.Methodology This research uses explanatory research that emphasizes the relationship between research variables through hypothesis testing.Research variables include temporal myopia, spatial myopia, failure myopia, and financial performance.Data collection was carried out using structured questionnaires, and analysis was performed using PLS-SEM with SMART-PLS software.The results showed that managerial myopia (temporal, spatial, and failure) negatively affects financial performance.These results indicate that the biased practices of management may affect the company's financial performance.This study contributes to the financial literature on managerial myopia by analyzing the influence of managerial myopia on financial performance, thereby increasing understanding of the phenomenon of managerial myopia in microfinance institutions, where previous research was still limited.Managers can easily use the indicators we developed to assess managerial myopia as metrics for each type of myopia.These empirical results serve as an impetus for myopic managers to redirect their behavior and help convince sustainability-oriented managers to stay on track.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".