Management project performance in Indonesia’s food estate development: A stakeholder, institutional, and communication perspectiv
Bibliographic record
Abstract
This study examines the factors influencing the performance of food estate projects in Indonesia, focusing on the roles of leadership, stakeholder engagement, institutional support, and project communication. A positivist research paradigm was adopted, employing a quantitative approach with Structural Equation Modeling (SEM-PLS) to test the hypotheses and analyze the relationships between these variables. The study found that leadership significantly impacts project performance, particularly through its influence on project communication, which serves as a key mediator. While stakeholder engagement did not show a direct significant relationship with project communication, its role in fostering trust, reducing resistance, and ensuring stakeholder needs are met is crucial for overall project success. Institutional support directly contributes to project performance by providing resources, supportive policies, and technical assistance, thereby enhancing the effectiveness of project communication. Project communication, as a mediator, integrates leadership, stakeholder engagement, and institutional support to drive successful project outcomes. These findings underscore the importance of transformational leadership, effective communication, and institutional support in improving food estate project performance in Indonesia. The results offer valuable insights for practitioners and policymakers aiming to enhance project outcomes through strategic management practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".