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Record W4393870831 · doi:10.3390/admsci14040069

The Mediating Effect of Motivation between Internal Communication and Job Satisfaction

2024· article· en· W4393870831 on OpenAlexfundno aff
Tânia Santos, Eulália Santos, Marlene Sousa, Márcio Oliveira

Bibliographic record

VenueAdministrative Sciences · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
FundersCentro Interdisciplinar de Ciências SociaisFederation for the Humanities and Social SciencesUniversidade da Beira Interior
KeywordsJob satisfactionInternal communicationsPsychologySocial psychologyJob attitudeBusinessJob performanceMarketing

Abstract

fetched live from OpenAlex

Communication in organisations is essential for them to be competitive in a global world that is constantly changing. Internal communication especially can be a highly effective and useful strategic tool for improving organisational performance through employee motivation and satisfaction. Based on a questionnaire survey completed by 426 employees of Portuguese organisations, this work aims to understand, using a partial least squares structural equation model, the importance of internal communication in the motivation and satisfaction of Portuguese employees. The results show that internal communication in organisations directly influences job satisfaction and also indirectly, through motivation at work, giving motivation at work the role of mediator. It is therefore important for Portuguese organisations to invest in effective internal communication strategies in order to promote employee motivation and satisfaction, recognising motivation as a key mediator in the relationship between internal communication and job satisfaction.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.163
GPT teacher head0.463
Teacher spread0.300 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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