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Record W2110203860 · doi:10.1177/001979390906200406

The Effects of Institutional and Organizational Characteristics on Work Force Flexibility: Evidence from Call Centers in Three Liberal Market Economies

2009· article· en· W2110203860 on OpenAlexaffabout
Danielle D. van Jaarsveld, Hyunji Kwon, Ann C. Frost

Bibliographic record

VenueIndustrial and Labor Relations Review · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsDismissalFlexibility (engineering)DiscretionWork (physics)Affect (linguistics)Labour economicsBusinessEconomicsIndustrial organizationPolitical scienceEngineeringLawSociologyManagement

Abstract

fetched live from OpenAlex

This comparative study examines survey data from 464 call centers in the United States, 167 in the United Kingdom, and 387 in Canada to explore two questions: whether institutional differences shape employers' choices of ways to improve work force flexibility, both numerical and functional; and whether strategies for numerical flexibility and functional flexibility are related. The results suggest that institutional differences across these liberal market economies—specifically, in dismissal regulations and union strength—did affect how employers chose to achieve work force flexibility. For example, the use of part-time workers was more common in countries with more stringent rules regulating dismissals. Organizational characteristics also mattered, with outsourced firms being more likely than in-house firms to use part-time workers. Evidence also suggests that managers used numerical flexibility and functional flexibility strategies as substitutes: higher employee job discretion was associated with both lower dismissal rates and a lower likelihood of temporary use.

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)0.002
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 score0.504
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
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 head0.031
GPT teacher head0.281
Teacher spread0.250 · 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

Citations20
Published2009
Admission routes2
Has abstractyes

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