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Record W1550404247 · doi:10.1108/19348830710868293

Appointing a general manager in Sino‐US joint ventures

2007· article· en· W1550404247 on OpenAlexaff
Xiaohua Lin

Bibliographic record

VenueInternational journal of organizational analysis · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOriginalityChinaSample (material)BusinessValue (mathematics)Joint (building)Functional managerControl (management)MarketingPublic relationsPsychologyManagementProject managementPolitical scienceEconomicsSocial psychologyComputer scienceProject portfolio managementEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine performance implications of general manager appointment in Sino‐US joint ventures, specifically whether there is a difference in outcomes when the appointment is made by the Chinese or American partner. Design/methodology/approach Using a structured questionnaire, data were collected from 94 managers representing US and Chinese partners in 67 international joint ventures (IJVs) based in China. Findings The results show that, when the general manager is Chinese rather than American, there is heightened conflict on daily personnel management issues, but not on strategic and contract issues, and the overall levels of partner satisfaction and relationship commitment decrease as well. Research limitations/implications The research was based on small sample size and cross‐sectional design. Originality/value This article focuses on the general manager appointment as a control mechanism and explores its link to IJV performance. It identifies daily/personnel issues as a source of conflicts that are associated with the right to appoint the IJV general manager.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.215
Teacher spread0.210 · 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 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

Citations4
Published2007
Admission routes1
Has abstractyes

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