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Record W4406453593 · doi:10.4018/jgim.367600

From Efficiency to Growth Strategy Along the Global Value Chains

2025· article· en· W4406453593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Global Information Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsLakehead UniversityUniversité Laval
Fundersnot available
KeywordsValue (mathematics)EconometricsIndustrial organizationBusinessMathematicsStatistics

Abstract

fetched live from OpenAlex

Offshore outsourcing has been considered a low cost production site. There is rare studies that addressed the offshore outsourcing strategy as a growth strategy where offshoring focal firm can develop their capabilities for competitive advantage. This paper explores how SMEs enhance their dynamic capabilities by entering into offshore outsourcing relationships. The dynamic capabilities development process includes increasing focus on the Core competency, developing innovation capabilities, increasing market share in existing and/or new markets, and improving its flexibility to face the dynamic business ecosystem. This exploratory case study on ten manufacturing SMEs from Quebec (Canada) shows that offshore outsourcing contributes to developing dynamic capabilities with varying degrees of success. It shows an evolutionary path of the dynamic capability development process. Managers can enhance their understanding on how offshoring can enable firms to improve their dynamic capabilities to face challenging business eco-system and remain competitive in the high cost countries (HCC).

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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