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Enregistrement W4414438535 · doi:10.38104/vadyba.2025.2.02

EMPLOYEE ENGAGEMENT ACROSS BORDERS: A GALLUP-BASED ANALYSIS OF GLOBAL WORKFORCE

2025· article· en· W4414438535 sur OpenAlexaboutno aff

Notice bibliographique

RevueJournal of Management · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLabor Movements and Unions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEmployee engagementEmployee resource groupsEmployee researchLoyaltyWorkforceWork engagementAntecedent (behavioral psychology)Human resource managementJob satisfactionMeaning (existential)

Résumé

récupéré en direct d'OpenAlex

Employee engagement, which concept has garnered substantial scholarly attention in recent decades and has been defined in various ways, with no single leading definition, can be basically described as an employees' emotional and cognitive connection to their work, workplace and its goals. Engaged employees fulfills their job responsibilities and actively participates in the development of the organization, feels a sense of belonging and sees meaning in their work, positively influencing organizational performance. Nowadays labor market, where uncertainty, remote work and generational diversity and competition between companies and organizations are increasing, employee engagement plays a vital role in the successful operation of organizations. Literature highlights employee engagement as one of the most important factors that positively impacts employees' work performance. The benefits from engaged employees are discovered in multiple levels - individual, organizational, customer. At the individual level, employee engagement is associated with improved work performance, as mentioned before, higher employee productivity, loyalty and retention, also employee innovative behavior, initiative and creativity. At the organizational level, engagement contributes to enhanced organizational performance, operational effectiveness and innovation. At the customer level, employee engagement drives better customer experience and satisfaction. Recent research indicates that employee engagement is not only an outcome influencing various factors and performance indicators, but also a construct shaped by multiple antecedent factors that serve to foster and sustain it human resource management practices, job satisfaction, work environment and also individual state of mind as mindfulness. The empirical part of the article about global employee engagement trends is developed by secondary data from Gallup's State of the Global Workplace report (2025). Analyzed data indicates a positive trend with periodic fluctuations during the period from 2009 to 2024, but last year has been decline in employee engagement metrics, highlighting challenges in organizational human resource management practices. Globally, only 21% of employees are engaged. The situation is particularly critical among managers, where engagement is declining, with young managers (under 35) and female managers experiencing the greatest decline. A strong trend towards a higher proportion of not engaged employees has persisted and prevailed in the analyzed period, while the engaged and actively disengaged has been in similar rates. Regional analysis reveals pronounced disparities in employee engagement levels. Within the European region, employee engagement remains at its lowest (13%), accompanied by elevated stress levels and diminished emotional well-being among employees. The data of Europe show a paradox: economically stronger countries demonstrate lower levels of employee engagement, while less developed countries or countries undergoing economic change show relatively higher levels of employee engagement. From the perspective of economic analysis in region of Europe, this distribution confirms that employee engagement does not directly depend on a country's gross domestic product or level of welfare. Conversely, the highest levels of employee engagement are observed in the US, Canada and Latin America and the Caribbean (31%), and this proportion remains relatively low in absolute terms.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,561
Score d'incertitude au seuil0,339

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,383
Écart entre enseignants0,360 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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