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Record W2019070176 · doi:10.1002/tie.1040

International expansion of e‐retailers: Where the Amazon flows

2001· article· en· W2019070176 on OpenAlexaffabout
Rajesh Chakrabarti, Barry Scholnick

Bibliographic record

VenueThunderbird International Business Review · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompetition (biology)Amazon rainforestProtectionismBusinessForeign direct investmentProduct (mathematics)DisadvantageRules of originInternational tradeMarket accessCommerceMarketingInternational economicsAdvertisingIndustrial organizationEconomicsCommercial policyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.243
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2001
Admission routes2
Has abstractyes

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