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Record W1605259068 · doi:10.1017/cbo9780511808722.006

The nature of home country location advantages

2009· book-chapter· en· W1605259068 on OpenAlexaff
Alain Verbeke

Bibliographic record

VenueCambridge University Press eBooks · 2009
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsForcing (mathematics)ProductivityIndustrial organizationKey (lock)Diamond modelBusinessEconomic geographyInternational tradeEconomicsPolitical scienceEconomic growthComputer scienceLaw

Abstract

fetched live from OpenAlex

This chapter explores Porter's idea that the most important aspect of international business strategy is four key home country location advantages, often simply referred to as ‘Porter's diamond’. Porter's idea is that, ultimately, an MNE's long-term competitiveness results from vigorous domestic pressure in its home base, forcing it to innovate and improve productivity. This idea will be examined and then criticized using the framework presented in Chapter 1. Significance In the early 1990s, Michael Porter's now-classic HBR article, ‘The competitive advantage of nations’ (and the identically named book) created substantial debate on the sources of international competitiveness. Porter argues that any company's ability to compete in the international arena is based mainly on an interrelated set of location advantages in its home country. A high level of pressure in its home base pushes the firm to innovate and to upgrade systematically, resulting in FSA creation. These FSAs are then instrumental to expansion in foreign markets. According to Porter, ‘a nation's competitiveness depends on the capacity of its industry to innovate and upgrade. Companies gain advantage against the world's best competitors because of pressure and challenge. They benefit from having strong domestic rivals, aggressive home-based suppliers, and demanding local customers.’ According to Porter, FSAs are primarily developed not because firms have a strong, internal entrepreneurial drive, or because they can easily access external resources, but because they face external pressure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.002

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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2009
Admission routes1
Has abstractyes

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