TRACKING THE EVOLUTION OF E-GROCERS: A QUANTITATIVE ASSESSMENT
Bibliographic record
Abstract
Forecasts of the proportion of food retailing likely to be conducted over the Internet remain small, perhaps only contributing 2 percent of sales. One reason for this low market share is the challenge E-Grocers face in developing strategies which respond to four key areas of interest to consumers: signals of firm quality; signals of product quality; the range of products offered; and service, or customer-relationship management (CRM). Careful attention to these consumer concerns is important in all retail relationships-online or offline. This paper compares indicators of these factors across U.S. E-Grocers. A quantitative four-period ranking of online food-retailing strategies is presented for the nascent industry. Data from the third and fourth quarters of 2001, the fourth quarter of 2002, and the first quarter of 2004 provide the basis of this discussion. After initial setbacks, data show traditional ("bricks") grocery retailers successfully developing online strategies. Firms not primarily focused on groceries exited the E-Grocery sector, while the development of specialty food suppliers blurred the concept of online food retailing. Gaps in current strategies are indicated using content analyses of E-Grocery web sites.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".