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Record W178937420 · doi:10.25300/misq/2013/37.1.08

Internationalization Strategies of Chinese It Service Suppliers1

2013· article· en· W178937420 on OpenAlexaff
Ning Su

Bibliographic record

VenueMIS Quarterly · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsWestern University
Fundersnot available
KeywordsInternationalizationBusinessMultinational corporationEmerging marketsIndustrial organizationChinaService (business)ExploitOutsourcingProcess (computing)MarketingCommerceInternational tradeFinance

Abstract

fetched live from OpenAlex

With China emerging as a new frontier of global IT outsourcing, many Chinese IT service suppliers are actively expanding in three major markets: Asia, especially Japan, the West, especially the United States, and the Chinese domestic market. Compared to multinational suppliers and established Indian suppliers, Chinese IT service firms are at a relatively early, but rapidly growing stage, which offers a unique opportunity to explore an understudied topic in the information systems literature: internationalization strategies of IT service suppliers from emerging economies. Through a three-part qualitative case study of 13 China-based IT service firms, including almost all of the Chinese suppliers recognized globally, this study elaborates the inter-nationalization behavior and decision rationale of these suppliers. The findings show that these major Chinese suppliers include both firms that incrementally internationalize and firms that are “born global.” For both types of firms, the entry and growth in different markets is a highly dynamic activity combining a strategically planned, resource-seeking process and a flexible, opportunistic bricolage process based on existing operation capabilities and client relationships. The suppliers dynamically oscillate between these processes to exploit and create opportunities while expanding in multiple 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2013
Admission routes1
Has abstractyes

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