The impact of financial support from non-resident family members on the financial performance of newer agribusiness firms in India
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of financial support from non-resident family members ( FSNRFM ) on the financial performance of newer agribusiness firms in India. Design/methodology/approach Owners of newer agribusiness firms (five years old or less) from India were surveyed regarding the perceived impact of FSNRFM on the financial performance of newer agribusiness firms. Findings The results show that newer agribusiness firms with FSNRFM perform better than those without FSNRFM ; and build higher levels of internal financing sources relative to the newer agribusiness firms without FSNRFM , which, in turn, improves their performance. Research limitations/implications This is a co-relational study that investigated the association between FSNRFM and financial performance of newer agribusiness firms. There is not necessarily a causal relationship between the two. The findings of this study may only be generalized to firms similar to those that were included in this research. Originality/value The study enriches the literature concerning newer agribusiness firms and the factors that improve their financial performance. The results of this study can be of great significance for owners of these firms, financial managers, farm management consultants, and other stakeholders to understand the impact of FSNRFM on financial performance of newer agribusiness firms.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".