The impact of participative budgeting on the supply chain resilience amid COVID-19 pandemic: Empirical evidence from Vietnam
Bibliographic record
Abstract
Disruptive impact as the Covid-19 pandemic reduces the motivation of managers working in the supply chain function. A motivation as organizational commitment is crucial for organizations to foster supply chain resilience through enhancement of internal and external supply chain integration. This study draws upon the budgeting literature to explore the role of participative budgeting on the supply chain resilience amid Covid-19 pandemic. Data were collected from 191 managers working in supply chain functions of organizations operating in industrial zones in Vietnam. The technique of partial least square structural equation modelling was used to assess data. The results suggest that Covid-19's disruptive impact increases participative budgeting, which results in organizational commitment. This commitment leads to the enhancement of internal and external supply chain integration, which in turn leads to supply chain resilience. This study is the first study to explore how and why budgeting practices lead to the enhancement of supply chain resilience amid Covid-19 pandemic.
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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 | 0.004 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".