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Record W2066392544 · doi:10.1108/13527591111159036

Contingent workers: needs, personality characteristics, and work motivation

2011· article· en· W2066392544 on OpenAlexaffabout
Vlad Vaiman, Jeanette Lemmergaard, Ana Azevedo

Bibliographic record

VenueTeam Performance Management · 2011
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPersonalityHuman resource managementOriginalityWork (physics)Context (archaeology)Value (mathematics)PsychologyHuman resourcesWork motivationOrder (exchange)Knowledge managementMarketingBusinessManagementSocial psychologyComputer scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

Purpose This paper seeks to challenge the claim that traditional and non‐traditional employees differ significantly in terms of their needs, personality characteristics, and work motivation patterns, by surveying management consultants in Canada. Design/methodology/approach The study is based on a quantitative online survey undertaken among 204 Canadian management consultants in 2008, representing both traditional employed consultants, contingent consultants, and company representatives. Findings The study demonstrated no significant differences with regard to needs, motivation, and personality characteristics between traditional and non‐traditional employed management consultants, which means that no significant changes to existing human resource management policies seem to be needed. Originality/value The existing literature on contingent employees' needs, personality characteristics and work motivation has mainly been devoted to the study of differences between traditional and non‐traditional work arrangements seen as single groups. This study extends and complements the understanding of the underlying dimensions of both the explicit and the implicit contract within the contingent management consultant‐organization relationship in order to explain the influence of these dimensions on the human resource management strategies. The underlying assumption is that non‐traditional work arrangements vary according to the type of job and the context in which the job is performed.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.262
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.322
Teacher spread0.237 · 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 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

Citations17
Published2011
Admission routes2
Has abstractyes

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