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Record W2799908824 · doi:10.1108/jadee-04-2016-0022

The impact of financial support from non-resident family members on the financial performance of newer agribusiness firms in India

2018· article· en· W2799908824 on OpenAlexaff
Amarjit Gill, Harvinder S. Mand, John D. Obradovich, N. D. Mathur

Bibliographic record

VenueJournal of Agribusiness in Developing and Emerging Economies · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAgribusinessBusinessValue (mathematics)OriginalityFinanceMarketingAccountingAgricultureQualitative research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.241
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agribusiness in Developing and Emerging EconomiesSame topicFamily Business Performance and SuccessionFrench-language works237,207