The role of cultural capital in improving the financial performance of village credit institutions
Bibliographic record
Abstract
The Village Credit Institution (LPD) is an organization of micro finance which is in the scope of Balinese traditional villages. More specifically, LPDs whose members are indigenous people are very strong with a culture that grows and develops in society, so that activities in achieving organizational goals can be achieved through collaboration of cultural capital. The desired achievement describes organizational structure, cultural capital, and credit risk in improving financial performance which places cultural capital as a moderating variable in LPD in Bali. Quantitative methods are used in this study, namely selecting specific cases on the structure of the role of cultural capital for the organization. Quantitative data processing using SEM-PLS, the number of samples includes 100 respondents obtained through a questionnaire. The results of this study indicate that cultural capital plays an important role in LPD activities, especially related to organizational structure, credit risk, efficiency, and financial performance. Even though in a pandemic condition, the LPD spends to provide massive assistance, this proves that the LPD is a hybrid organization which also emphasizes a social perspective with an emphasis on wisdom. Novelty raised in this research is the emergence of collaboration patterns between LPDs and customers in the framework of common interests. This research has implications for LPD as a reference for positive perspectives on the role of cultural capital on organizational dynamics. Furthermore, this research can provide input to local governments that local regulations are needed that can assess social activities carried out by LPDs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".