International expansion of e‐retailers: Where the Amazon flows
Bibliographic record
Abstract
Abstract Competition among e‐retailers is becoming increasingly intense and continuously narrowing margins. We examine the price competition between Amazon and Barnes & Noble to illustrate this. The key to survival, therefore, lies in rapidly expanding market size in one or both of two ways: expansion across product lines and international expansion. Although foreign consumers can easily access an online retailer's website, high cross‐border shipping costs often put foreign e‐retailers at a disadvantage. We show how, in spite of being a superior store in most respects, Amazon fails to compete with the leading Canadian online book retailer, Chapters Online, primarily because of cross‐border shipping disadvantages. Consequently e‐retailers are using traditional foreign direct investment (FDI)‐based strategies to expand internationally. In addition, a new mode of foreign market entry—e‐mediation—involving match‐making between customers and suppliers in a country through a website located in another country, is also emerging. This is particularly suitable in situations where protectionist barriers forbid FDI‐based strategies. Amazon plans to use this strategy in Canada. © 2002 John Wiley & Sons, Inc.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".