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Record W2118696112 · doi:10.1177/1038411102040003255

The Role of Transaction Costs and Institutional Forces in the Outsourcing of Recruitment

2002· article· en· W2118696112 on OpenAlexaff
Marie T. Dasborough, C. Sue-Chan

Bibliographic record

VenueAsia Pacific Journal of Human Resources · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOutsourcingTransaction costBusinessAgency (philosophy)Knowledge process outsourcingIndustrial organizationFunction (biology)Institutional theoryAgency costPrincipal–agent problemService (business)Business administrationMarketingEconomicsFinanceManagement

Abstract

fetched live from OpenAlex

This study investigated reasons for the outsourcing of a core HRM function, recruitment. Drawing from transaction costs and institutional theories, it was hypothesised that the pressure to minimise transaction costs and the presence of industry trends towards outsourcing would be positively associated with the outsourcing of recruitment. Survey data were gathered from 1I 7 HR professionals in Australia. Both hypotheses were partially supported. Specifically, the outsourcing of recruitment activities was positively associated with trust in the agency supplying the recruitment service and with the need to reduce internal labour but not fixed costs. With regard to institutional theory, the outsourcing of recruitment was positively associated with mimetic but not coercive forces. The study concludes that although most assumptions about recruitment agency use are expressed in economic terms, in reality, HRM practices are also influenced by forces exerted by the institutional environment in which organisations are located.

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)0.013
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.064
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.005
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.024
GPT teacher head0.228
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; 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 designTheoretical or conceptual
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

Citations25
Published2002
Admission routes1
Has abstractyes

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