Business Model Innovation for Ambulance Systems in Low- and Middle-Income Countries: “Coordination and Competition”
Bibliographic record
Abstract
Several low- and middle-income countries’ emergency transportation systems (ETSs) do not have a centralized emergency number. Instead, they have many independent ambulance providers, each with a small number of ambulances. As a result, ETSs in these contexts lack coordination and ambulances. Using a free-entry equilibrium model, we show that in such decentralized systems, the probability that any given call can be served by at least one ambulance, that is, its coverage, is at most 71.54%, regardless of the ETS’s profitability. We examine three business models that can address the ETS’s lack of coordination and ambulances: (i) a competitor-only business model, where an entrepreneur enters the ETS and acquires ambulances to compete with existing providers; (ii) a platform business model, where an entrepreneur coordinates existing providers; and (iii) an innovative platform-plus business model, where an entrepreneur combines (i) and (ii): setting-up a platform and acquiring platform-owned ambulances. We also examine a government-run platform that takes no commissions from providers. Using a game-theoretic approach, we find that it is optimal for all platform models to incentivize all providers to join. However, only the government-run platform may incentivize providers to acquire additional ambulances. Furthermore, a government-run platform offers higher coverage than a platform-plus only when the platform’s power to coordinate ambulance providers is moderate. Our results can help entrepreneurs and policymakers in LMICs navigate various tradeoffs in improving their countries’ ETS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".