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Record W2034698958 · doi:10.1002/tie.20283

How market orientation and outsourcing create capability and impact business performance

2009· article· en· W2034698958 on OpenAlex
Satyendra Singh

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.

Bibliographic record

VenueThunderbird International Business Review · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsOutsourcingMarket orientationBusinessCLARITYIndustrial organizationKnowledge process outsourcingOrientation (vector space)CommerceMarketing

Abstract

fetched live from OpenAlex

Abstract For most firms, developing the capability to compete and perform is crucial. The literature suggests that market orientation and outsourcing are two such sources for building capabilities in the marketplace. However, the relative contribution of market orientation and outsourcing to capability and superior business performance is unclear. To bring clarity, two pathways through which market orientation and outsourcing build capability and enhance business performance are proposed. Using data from foreign and Indian firms, the results indicate that both market orientation and outsourcing contribute to building capability, and that outsourcing further contributes to business performance. Also, it was discovered that low‐risk market‐oriented and high‐risk outsourcing firms experienced a positive impact on business performance. The implication of these results for managers is that market orientation and outsourcing can be complementary tools in their efforts to build capability, enhance business performance, and manage risky environmental conditions. © 2009 Wiley Periodicals, Inc.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.014
GPT teacher head0.247
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