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Record W2516133753 · doi:10.1108/ijopm-04-2015-0212

Survival strategy of OEM companies: a case study of the Chinese toy industry

2016· article· en· W2516133753 on OpenAlexaff
Dezhi Chen, William Wei, HU Dai-ping, Etayankara Muralidharan

Bibliographic record

VenueInternational Journal of Operations & Production Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMacEwan University
Fundersnot available
KeywordsOriginal equipment manufacturerUpgradeBusinessIndustrial organizationMarketingValue (mathematics)Operations managementEconomicsComputer science

Abstract

fetched live from OpenAlex

Purpose Although there have been many discussions on the status and development of original equipment manufacturers (OEMs), theory on how they survive is minimal. Little is known about how OEMs survive and upgrade to other business models, such as original design manufacturers (ODMs) and original brand manufacturers (OBMs), in emerging economies. The purpose of this paper is to extend the theory on the survival path of OEMs from the perspective of emerging countries by examining how OEMs survive cost pressures and upgrade to ODMs or OBMs. Design/methodology/approach Using a multi-case study method, this study analyzes the survival path employed by OEMs by examining eight firms in the Chinese toy industry. Findings This study shows that OEMs remain weak in the global toy industry chain due to labor costs. While some OEMs move to low-cost regions, others turn to OBM management, after transitioning through an ODM model, by investing in research and development and marketing. Originality/value This study explores the survival paths of OEM enterprises, showing that OEMs can first upgrade to ODMs and then to OBMs, or they can directly upgrade to OBMs. Shifting from OEM to ODM is an important step in the transition process, although the contract that OEMs have with their foreign partners does not change significantly.

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 score0.481
Threshold uncertainty score0.323

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 science0.0010.000
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 head0.029
GPT teacher head0.295
Teacher spread0.266 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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