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Record W2746135981 · doi:10.5539/ibr.v10n9p96

The Cross-Culture Management of Chinese Enterprises in Poland Under the Belt and Road Initiative—Based on PEST Model

2017· article· en· W2746135981 on OpenAlexvenueno aff
Guiyu Dai, Yi Cai

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)BusinessHofstede's cultural dimensions theoryInvestment (military)PoliticsChinese cultureChinaMarketingEconomic geographyIndustrial organizationPolitical scienceSociologyEconomicsManagementSocial science

Abstract

fetched live from OpenAlex

“The Belt and Road Initiative” not only provides great opportunities but also poses enormous challenges to Chinese enterprises for further development. Along the Belt and the Road, there are different countries with unique culture characteristics, which will be the difficult challenges Chinese enterprises have to face in the overseas investment. The present study will combine PEST (Political, Economic, Social and Technological) model with Hofstede’s culture dimensions as the theoretical basis for analyzing the potential opportunities and challenges Chinese enterprises tend to confront in Poland. Based on a detailed analysis of the opportunities and challenges, this writing proposes three tentative cross-culture management strategies: (1) Investigating the local markets and identifying the culture differences; (2) Cultivating intercultural communication competence of the cross-culture employees; (3) Acculturating to the local society and making innovation based on culture fusion, which would be referential for Chinese enterprises to seek investment opportunities in the countries along the Belt and the Road.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.064
GPT teacher head0.378
Teacher spread0.313 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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