The Role of Human Capital in Strengthening Horticultural Agribusiness Institutions: Evidence from Structural Equation Modeling
Bibliographic record
Abstract
Farmers' engagement in agribusiness institutional activities has largely been confined to production activities, and has not been fully optimized. Similarly, the role of agricultural extension workers in providing institutional assistance has been narrowly scoped, mostly limited to government-initiated programs focusing on infrastructure development and production enhancement. Human capital, a vital factor for agribusiness development, has been largely overlooked. This study, therefore, seeks to investigate the influence of human capital components on the strengthening of horticultural agribusiness institutions, with farmer participation and coordination between farmer institutions as mediating factors. The research was conducted in the Uluere Sub-district, Bantaeng District, South Sulawesi Province, Indonesia, a region known for horticultural agribusiness development. Data from 120 randomly selected respondents were analyzed using Structural Equation Modelling (SEM) to accomplish the research objective. The findings revealed that leadership, a component of human capital, has a direct, positive, and statistically significant influence on institutional strengthening. However, participation does not serve as a mediator between human capital components and the strengthening of horticultural agribusiness institutions. The variable of coordination function partially mediates between leadership and institutional strengthening, while the effectiveness of teamwork fully mediates the impact on institutional strengthening.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".