Omnichannel Battle between Amazon and Walmart: Is the Focus on Delivery the Best Strategy?
Bibliographic record
Abstract
A large body of academic research has recently focused on omnichannel retailing especially on brick-and-mortar (offline) retailers adding and integrating online capabilities. Relatedly, trade press has highlighted how offline retailers have been investing heavily in the use of their existing physical retail network for quicker delivery and pick-up of online orders. Looking at the competition between Amazon and Walmart, however, we demonstrate that focusing on quicker delivery is not the best strategy for offline retailers when opening online channels to compete with online retailers. We estimate a multivariate probit model using data from a customer survey and find that offline retailers should instead focus on delivering the fundamentals of retailing to their online customers too – larger assortment, competitive prices, and purchase convenience. Further, we employ cluster analysis to show which demographics are good targets for retailers as they develop omnichannel capabilities, as well as which demographics retailers need to keep loyal to their original channels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".