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Record W2258152898 · doi:10.1080/09585192.2016.1138316

An ill-informed choice: empirical evidence of the link between employers’ part-time or temporary employment strategies and workplace performance in Canada

2016· article· en· W2258152898 on OpenAlexafffundabout
Işık U. Zeytinoglu, James Chowhan, Gordon B. Cooke, Sara L. Mann

Bibliographic record

VenueThe International Journal of Human Resource Management · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of GuelphMemorial University of NewfoundlandMcMaster University
FundersMemorial University of NewfoundlandUniversity of Guelph
KeywordsProfitability indexProductivityFlexibility (engineering)RevenueBusinessLabour economicsWorking timeEmpirical researchOperations managementEconomicsFinanceWork (physics)ManagementEconomic growth

Abstract

fetched live from OpenAlex

Many employers seek flexibility through part-time or temporary employment to achieve improved competitiveness and success. Using strategic choice theory, this study is a longitudinal examination of employers' strategic decisions of reducing labour costs and using part-time or temporary workers on workplace performance. Workplace performance is measured through profitability, productivity and change in net operating revenue. Statistics Canada's Workplace and Employee Survey longitudinal workplace data are used for the analysis. Results show that reducing labour costs strategy has no effect on profitability, productivity or change in net operating revenue, and using part-time or temporary workers strategy shows decreased profitability and productivity, and that there is no effect on the change in net operating revenue in Canadian workplaces studied. Based on these findings, we recommend that employers, in Canada and elsewhere, not only carefully weigh reducing labour costs and employing part-time or temporary workers strategies for workplace performance, but also reconsider such strategies and instead seek alternative means of improving workplace performance.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.107
GPT teacher head0.406
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2016
Admission routes3
Has abstractyes

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