The mediating effect of crisis leadership and digital technologies on emergency supply chain capabilities of logistic companies
Bibliographic record
Abstract
This research aims to analyze the relationship between crisis leadership and supply chain emergencies. The study also investigates the effect of digital technology on supply chain emergencies and the effect of digital technology on crisis leadership. The research method is a quantitative approach, research data was obtained by distributing online questionnaires via social media. There were 856 questionnaires distributed to logistics company managers and of the 856 questionnaires distributed, 426 respondents or 50% gave responses determined by the simple random sampling method. This research uses quantitative methods with data analysis using Structural Equation Modeling (SEM) through Partial Least Square (PLS) with data processing software, SmartPLS 3.0. The results of this research show that crisis leadership had a positive and significant influence on supply chain emergencies. Digital technology had a positive and significant influence on supply chain emergencies and digital technology had a positive and significant influence on crisis leadership. In addition, crisis leadership mediated the relationship between digital technology and supply chain emergency capability. Crisis leadership had a fully mediated nature. Crisis leadership encourages an increase in the relationship between digital technology and emergency supply chain capability. These findings create the view that the application of digital technology and crisis leadership can encourage improvements in emergency supply chains and provide direction to logistics company managers to use digital technology to improve supply chain capabilities in their companies. The findings of this research indicate that digital technology under the influence of crisis leadership significantly improves emergency supply chain capabilities.
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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 | 0.001 | 0.012 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".