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Record W2978545540 · doi:10.22215/etd/2017-12098

The Indirect Effect of Employee Entitlement: A Career Stage Perspective

2017· dissertation· en· W2978545540 on OpenAlexaffabout
Gary Lawlor

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsEntitlement (fair division)Organizational justiceMediationPsychologyDistributive justiceInteractional justiceSocial psychologyInterpersonal communicationProcedural justiceEmployee engagementEconomic JusticeGovernment (linguistics)Perspective (graphical)Public relationsPolitical scienceOrganizational commitmentEconomics

Abstract

fetched live from OpenAlex

The moderating role of career stage on employee entitlement and the mediating role of organizational justice on the relationship between employee entitlement and outcomes of work engagement and counterproductive work behaviours were examined with a sample of 624 Canadian government and North America employees.Contrary to expectations, career stage did not significantly impact levels of employee entitlement.Likewise, full scale organizational justice was not found to be a significant mediator; however, some of the subscales were.Distributive, procedural, and interpersonal justice demonstrated a significant mediation of the effect of employee entitlement on work engagement; procedural and interpersonal justice demonstrated a significant mediation of the effect of employee entitlement on counterproductive work behaviour.Findings suggest the importance of facets of organizational justice in mediating the effect of employee entitlement and career stage plays no significant role in the development of employee entitlement.

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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.277
Teacher spread0.263 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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