Digital transformation in public-private collaborations: The success of humanitarian supply chain operations
Bibliographic record
Abstract
Recent years have seen the extensive use of big data analytics, related technological infrastructure, and machine learning applications for digital transformation. The resource dependency related to data-driven applications elicits public-private collaborations (PPCs) between governments and private or non-government organizations (NGOs) for value creation. Such collaborations are effective for the success of humanitarian supply chain operations (HSCOs), particularly in the event of large-scale disasters. By building on resource dependence theory (RDT), our study explores the links between digital transformation, PPCs, and HSCO success. Using structural equation modeling on data collected from 224 key decision-makers and experts, we found that digital transformation mediates the relationship between private-NGO collaborations and HSCO success while host government support moderates it. Our study thus makes an original contribution to RDT and the emerging domains of contemporary digital and data-driven applications in HSCO. The implications and future research directions arising from this study are also discussed in this research paper.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".