Final deliveries for online shopping : French operators’ strategies according to the customers and the area they live in
Bibliographic record
Abstract
A striking element of e-commerce is that it is now widespread along the different segments of the population in developed economies, whether they live in central, suburban or even rural areas. Over the past ten years, this transformation has generated a significant demand for dedicated delivery services to end consumers. E-commerce results in an increasingly difficult physical distribution of products, with direct effects on logistics organizations in urban and suburban areas, where traffic congestion and accessibility are crucial factors. In the end-delivery sector, pick-up points and locker boxes represent a fast-growing solution, becoming a key element in the strategy of e-commerce distribution as alternative of home-delivery. They now represent about 20% of e-commerce deliveries in France, one of the highest rates in Europe. The purpose of this article is to identify how operators in charge of delivering e-commerce products adapt to different urban, suburban and rural environments. In what ways do pickup point delivery networks differ in dense urban areas and more sparsely populated suburban and rural environments? We focus our research on the department of Seine-et-Marne, in the East of the Paris region. After a literature review, we proceeded by conducting interviews with operators. Sixteen in-depth interviews have been conducted with the main transport operators involved in e-commerce in France. We finally conducted a spatial analysis and then we drafted a conceptual framework for pickup point networks. In addition to providing a better knowledge on current delivery processes for e-commerce, our work intends to contribute to urban planning by providing indicators on deliveries for e-commerce according to different residential environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".