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Record W7132253849

Transsion Holdings: Leveraging Disruption in Emerging Markets

2020· other· W7132253849 on OpenAlexaff
Taiyuan 王泰元, 赵丽缦, S. Ramakrishna Velamuri

Bibliographic record

VenueCEIBS Institutional Repository · 2020
Typeother
Language
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsEmerging marketsEmerging technologiesGovernment (linguistics)Context (archaeology)Work (physics)
DOInot available

Abstract

fetched live from OpenAlex

传音控股公司是一家中国企业,在低成本生产高品质产品方面拥有强大的优势。本案例描述了该公司如何通过颠覆式创新在非洲和其他新兴市场(如印度)实现了迅猛发展。作为一家资源有限的初创企业,传音成功超越了全球手机品牌(如三星和诺基亚)并占据了非洲手机市场的领先地位(按市场份额计)。但传音近年来面临着激烈的竞争。2019年1月,中国知名的智能手机品牌小米在印度市场站稳脚跟后随即进入了非洲市场,对传音保持非洲市场的领先地位构成了威胁。在印度,传音不仅要跟小米竞争,还要跟许多本土咄咄逼人的品牌竞争。此外,由于消费升级,功能手机正在让步于智能手机。作为全球最大的功能手机品牌,传音如何才能在此激烈的竞争中继续扩展业务? 基于克莱顿?克里斯坦森博士的颠覆式创新理论,本案例将引导学生讨论为什么传音能够成功进入非洲市场并占据领先地位,以及传音如何才能应对其他新兴市场的激烈竞争。通过讨论,本案例旨在探索技术型企业新兴市场成长战略的影响。

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.010
Scholarly communication0.0170.019
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0230.002

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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