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Record W4225336142 · doi:10.1504/ejim.2022.120701

Understanding the differences between Chinese and Western business practices: insights into Confucian philosophy

2022· article· en· W4225336142 on OpenAlexaff
Nibing Zhu, Zhilin Yang, Shaohan Cai, Haohao Sun

Bibliographic record

VenueEuropean J of International Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsCarleton University
Fundersnot available
KeywordsRighteousnessConfucian ethicsChinaPower (physics)SociologyBusiness ethicsCognitionExpression (computer science)Chinese philosophyEpistemologyPsychologyPolitical sciencePhilosophyPublic relationsLawTheology

Abstract

fetched live from OpenAlex

Confucian philosophy, which lies at the root of Chinese culture, has been attracting attention from both business practitioners and academia due to China's tremendous influence on the global economy. In this paper, we review the historical development of Confucianism and its managerial implications in China. We first identify key differences between Confucian and Anglo-American culture in terms of values and beliefs, power distance, cognitive patterns, social orientation, trust, communication, expression-orientation and social environment. We then highlight managerial implications of the five constant virtues inherent in Confucian philosophy, namely, benevolence ('ren'), righteousness ('yi'), rites ('li'), wisdom ('zhi') and trustworthiness ('xin'). A deep understanding of differences between Confucian and Anglo-American culture forms the foundation for mutually acceptable behavioural communication codes encompassing values and norms, cognitive patterns, social orientation patterns, modes and expression-orientation models. Finally, a case study is presented to illustrate how these principles are embedded within customer relations and organisational management.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.458

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 communication0.0000.001
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 head0.102
GPT teacher head0.280
Teacher spread0.178 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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