Impact of Efficiency on Voluntary Disclosure of Non-Banking Financial Company—Microfinance Institutions in India
Bibliographic record
Abstract
This paper investigates how the financial and social efficiency of firms influence the extent of the voluntary disclosure of Non-Banking Financial Companies–Micro Financial Institutions (NBFC-MFI). The study constructed an unweighted index of voluntary disclosure to estimate the level of voluntary disclosure of all of the included firms from the years 2015–2019. The financial and social efficiency, which is analogous to the technical efficiency of production theory and analyses both sustainability and outreach, respectively, was estimated using data envelopment analysis (DEA). The panel data analysis was completed, and a positive association of financial efficiency was estimated. The social efficiency was found to have no relationship to the voluntary disclosure level. This paper contributed to the literature by providing new determinants of voluntary disclosure. The study examines the econometric model and suggests that financially sustainable firms that utilize these resources well are more open to outsiders, while socially efficient firms are reluctant to voluntary disclosure, which also includes social activities, and consider this as a wasteful activity. The findings of this study are relevant to industry practitioners and regulators, who need to think upon the sustainability of this crucial sector by meeting the dual objectives of financial and social performance. This study is helpful to all stakeholders as well as for the government, who can use the results to design additional rules for the NBFC–MFI. This study will also help firms to design disclosure strategies to ascertain goodwill and less cost of capital, with easy access to funds.
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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 | 0.017 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".