MétaCan
Menu
Back to cohort
Record W2170386732 · doi:10.5539/ibr.v7n1p23

The Culture Shock and Cross-Cultural Adaptation of Chinese Expatriates in International Business Contexts

2013· article· en· W2170386732 on OpenAlexvenueno aff
Ling Shi, Lei Wang

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCollectivismHofstede's cultural dimensions theoryAdaptabilityAdaptation (eye)International businessUncertainty avoidanceCross-culturalContext (archaeology)Cross-cultural communicationShock (circulatory)IndividualismCultural diversityBusinessChinese cultureSociologyLanguage barrierCultural valuesPublic relationsPsychologySocial psychologyChinaPolitical scienceManagementSocial scienceEconomics

Abstract

fetched live from OpenAlex

This thesis aims to investigate the influential causes of culture shocks experienced by Chinese business expatriates, and meanwhile to reveal their difficulties in the cross-cultural adaptation in international business contexts. The research was conducted on the base of a semi-structured interview and an on-line survey among 80 Chinese business expatriates who came from a wide range of corporations and organizations. Through a quantitative-and-qualitative analysis, eight major influential causes of culture shock were identified, namely, business communication, language, individualism, collectivism, power distance, time orientation, religion, and tradition. The study also found that all Chinese expatriates encountered some cultural shocks in international business context and were greatly affected by communication, language, religious and traditional issues. The study further revealed three major difficulties in the cross-cultural adaptation: poor adaptability of business communication, language barriers and heavy pressure from work duties. Finally, based on the findings, several effective measures for a better cross-cultural adaptation were proposed.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.062
GPT teacher head0.437
Teacher spread0.375 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicInternational Student and Expatriate ChallengesFrench-language works237,207