Entrepreneurial behaviour formation among Farmers
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how farmers perceive and exploit business opportunities to foster entrepreneurship in developing country agriculture. Design/methodology/approach In total, 174 milk producers completed a face-to-face survey within a posttest- pretest research design. Partial least squares structural equation modelling (PLS-SEM) was used to test the hypotheses. Findings Results revealed that intentions, channelled through desirability, feasibility and optimism, become a strong predictor to recognise the opportunity to be entrepreneurial; however, the presence of a munificent environment and participation in apprenticeship and training programmes are the main and direct source of exploiting farming business opportunities. Research limitations/implications The major limitation of the study is that cross-sectional data collected only from milk producers in Pakistan, signifying a need to include other agricultural sectors across different developing countries for further contextualising the results. Originality/value Research on entrepreneurial behaviour among farmers is scant. This study emphasises how cognitive heuristics guide intentions influencing the process of opportunity formation, and a munificent environment and entrepreneurial skills trainings are necessary for starting dairy farming business with modern practices.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".