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Record W4285088540 · doi:10.1002/jcaf.22580

Is the global supply chain hard to reverse? Understanding manufacturing strategies of Chinese, Japanese, and South Korean Firms

2022· article· en· W4285088540 on OpenAlexaff
Fujiao Xie, Shirley J. Daniel, Ying Guo, Dongyoung Lee

Bibliographic record

VenueJournal of Corporate Accounting & Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlexibility (engineering)BusinessChinaSupply chainQuality (philosophy)Supply chain managementOrder (exchange)MarketingIndustrial organizationQuality managementOperations managementEconomicsFinanceManagementPolitical science

Abstract

fetched live from OpenAlex

Abstract As the Covid pandemic underscores global supply chain risks, there is a debate on whether to bring US manufacturing back from overseas. This paper provides insights into the heated debate on the global supply chain by examining the competitive manufacturing environments of China, Japan, and South Korea. More specifically, we conduct a cross‐national survey and empirically investigate the manufacturing strategies employed by manufacturing managers in the top Asian players: China, Japan, and South Korea. We examine four dimensions of the manufacturing strategies: quality, inventory, flexibility, and top management involvement. Our findings indicate that Japanese manufacturers are more committed to the cumulative approach to quality management and see enhanced flexibility as a strategic priority. While Chinese managers are also committed to achieving quality, they are more delivery‐driven and thus are more likely to occasionally accept slightly off‐quality components from suppliers to “save” an order. However, in all three countries, managers with a high focus on quality also focus on just‐in‐time management and in turn, on flexibility. There is significantly less agreement among Chinese managers, compared to their Japanese and Korean counterparts, that the top management should be involved in operational planning, goal setting, and the provision of rewards.

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)0.002
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 score0.200
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.241
Teacher spread0.199 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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