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Record W2071136065 · doi:10.1142/s0218927509001273

Knowing When to Merge: A Small IT Business in Korea Considers Its Options

2009· article· en· W2071136065 on OpenAlexaff
Chansoo Park

Bibliographic record

VenueAsian Case Research Journal · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMerge (version control)BusinessIndustrial organizationAllianceBackupComplementary assetsMarketingStrategic allianceInternational tradeComputer science

Abstract

fetched live from OpenAlex

This case study focuses on how a Korean software firm, CCMedia, executed a successful global strategy by merging with its technology partner to gain access to international markets. The case study also reviews the key challenges CCMedia faced after the merger. Intangible assets, such as IT technology, could allow CCMedia to earn overseas capital investment through the merger. With capital and human resources backup from IT Inspire Inc., its former technology partner, CCMedia could enter foreign markets. This case examines the transformation of a strategic technology alliance to a hierarchical structure as a result of a merger. It shows that technology-related alliances could play an important role in possible takeover activities. It provides insights into strategies that technology-based small businesses in Korea could follow to enter international markets.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.128
GPT teacher head0.361
Teacher spread0.233 · 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.

Study designNot applicable
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

Citations0
Published2009
Admission routes1
Has abstractyes

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