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Record W6926356045 · doi:10.25384/sage.c.5563163

Linking Work Values Profiles to Basic Psychological Need Satisfaction and Frustration

2021· other· en· W6926356045 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2021
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFrustrationWork (physics)Job satisfactionSample (material)Industrial and organizational psychologyLatent variableWork motivation

Abstract

fetched live from OpenAlex

The association between work values and key motivational variables has been repeatedly supported in previous studies. However, little attention has been devoted to understanding intraindividual patterns of work values and how combinations of work values relate to other motivational variables. This study aimed to identify profiles of work values based on a four-factor model (i.e., intrinsic, extrinsic, social, and status). It also investigated how profile membership relates to basic psychological need satisfaction and frustration at work using a self-determination perspective. A sample of French Canadian adults (N = 476) participated in this study by filling out an online questionnaire. Latent profile analyses revealed five distinct work values profiles. Results showed that participants in more positive profiles (i.e., high level of intrinsic, social, and status work values) generally reported higher level of need satisfaction and lower level of need frustration at work than participants belonging to more negative profiles (i.e., low level of intrinsic, social, and status work values). These results support the importance of considering work values in organizational and career development interventions, and to do so using a person-centered approach, to better understand need satisfaction and frustration at work.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0600.002

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 head0.146
GPT teacher head0.309
Teacher spread0.163 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueSage Journals DataSame topicDiverse Scientific and Economic StudiesFrench-language works237,207