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Record W4220795480 · doi:10.1108/mrjiam-07-2021-1206

The multidimensional work motivation scale: psychometric studies in Portugal and Brazil

2022· article· en· W4220795480 on OpenAlexaff
Nuno Rebelo dos Santos, Lisete Mónico, Leonor Pais, Marylène Gagné, Jacques Forest, Patrícia Martins Fagundes Cabral, Tânia Ferraro

Bibliographic record

VenueManagement Research The Journal of the Iberoamerican Academy of Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychologyDiscriminant validityConfirmatory factor analysisPortugueseScale (ratio)Measurement invarianceConvergent validitySocial psychologyOriginalityReliability (semiconductor)Brazilian PortuguesePsychometricsStructural equation modelingDevelopmental psychologyStatisticsMathematicsInternal consistencyCreativityGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to present validation evidence of a Portuguese version of the Multidimensional Work Motivation Scale, an instrument within the framework of the Self-Determination Theory, suitable for both Brazil and Portugal. Design/methodology/approach The current study demonstrates the suitability of this version in both Portugal ( N = 999) and Brazil ( N = 720). The authors applied confirmatory factor analyses (CFA) and tested the invariance between samples. Findings Results from CFA found the same structural dimensions as in the original study, invariant across both samples. Convergent and discriminant validity were shown through correlations between motivation subscales with affective commitment and emotional exhaustion. Originality/value Overall, the data provided strong evidence for the reliability and validity of the Portuguese version of the scale and reinforces the instrument as a cross-culturally valid measure of work motivation.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
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 head0.077
GPT teacher head0.360
Teacher spread0.283 · 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.

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

Citations18
Published2022
Admission routes1
Has abstractyes

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