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Foreign Market Corruption and the Structure of Subsidiaries in Emerging Markets

2016· article· en· W2767060735 on OpenAlexaff
Michael A. Sartor

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsNonmarket forcesLanguage changeBusinessBargaining powerMarket powerPrivate sectorInternationalizationSubsidiaryEquity (law)Industrial organizationMarket economyEconomicsMultinational corporationInternational tradeFactor marketFinanceMonopolyMicroeconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

We examine the relationship between host market corruption and the structure of equity-based foreign subsidiary investments. New constructs are introduced that categorize corruption into two dimensions - public corruption pervasiveness and private corruption pervasiveness. We theorize that firms will employ different entry strategies depending upon the pervasiveness of each type of corruption. The primary mechanism that drives the distinct approaches to foreign entry is the firm’s anticipated reliance on different sources of bargaining power to reduce information asymmetries that it expects to encounter in its public sector transactions and private sector transactions in the host market. By focusing on the role of a firm’s bargaining power in shaping its strategic foreign entry decisions under conditions of more pervasive host market corruption, our work contributes to efforts to more closely integrate market and nonmarket strategy research and to enhance our understanding of the strategic relevance of contemporary nonmarket phenomena such as corruption.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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