(Re)Solving Space and Time: Fulfilment Issues in Online Grocery Retailing
Bibliographic record
Abstract
There has been much hype and speculation in the media and in academe on the vitality and future of the ‘Internet economy’. In this paper the author uses case studies from Britain, Canada, New Zealand and the United States to assess the strengths and weaknesses of online grocery retailers, from national chain stores pursuing a ‘bricks and clicks' strategy to ‘pure-play’ startups. He argues that delivering groceries via the Internet to customer doorsteps requires ways of solving space and time that are markedly different from previous trends in food retail logistics. He holds that solving problems of space management creates problems in the management of time and vice versa. In particular, ‘e-tailers’ struggle with fulfilment costs and logistics, and have attempted to manage customers' time and locations to reduce these costs. Store-based operations may be best suited for short-term profitability (or loss minimisation), whereas warehouse-based fulfilment may hold future promise of greater efficiency and flexibility. The author suggests that online organic home delivery may be the most successful type of online food retailer, for its size, given greater customer commitment and problems with store-based supply of organic food.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".