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Investments by Emerging-Market Multinationals in Other Emerging Markets

2018· reference-entry· en· W2963676755 on OpenAlexaff
Jing Li, Daniel Shapiro

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmerging marketsForeign direct investmentMultinational corporationBusinessCorporate governanceLeverage (statistics)Spillover effectInternational businessNonmarket forcesInternational economicsEconomic systemEconomic geographyInternational tradeMarket economyEconomicsFactor market

Abstract

fetched live from OpenAlex

This chapter reviews the literature on foreign direct investments among emerging economies (E-E FDI), focusing on the motivations behind E-E FDI, country-specific advantages and firm-specific advantages associated with emerging-economy multinational enterprises (EMNEs), and spillover effects of E-E FDI on host-country economic and institutional development. We identify the following topics as posing important questions for future research: EMNEs’ ability to leverage home-government resources and diplomatic connections to promote investment in other emerging economies; nonmarket strategies of EMNEs in emerging economies; ownership and corporate governance affecting investment strategy and performance of EMNEs; E-E FDI contributions to sustainable development in host countries. Future studies should also consider potential heterogeneity among EMNEs by integrating insights from institutional theory, network theory, political science, corporate governance, corporate social responsibility, and sustainable-development research.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.019
GPT teacher head0.262
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

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