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Enregistrement W2983583444 · doi:10.1108/ijpl-05-2019-0018

Bureaucratic leadership, trust building, and employee engagement in the public sector in Ghana

2019· article· en· W2983583444 sur OpenAlexaff
Frank L. K. Ohemeng, Theresa Obuobisa‐Darko, Emelia Amoako Asiedu

Notice bibliographique

RevueInternational Journal of Public Leadership · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePublic Policy and Administration Research
Établissements canadiensConcordia University
Organismes subventionnairesnon disponible
Mots-clésPublic relationsPublic sectorWorkforceBureaucracyQualitative propertyContext (archaeology)Data collectionEmployee engagementNorm (philosophy)Order (exchange)Developing countryBusinessPolitical scienceSociologyPoliticsComputer scienceEconomic growthEconomics

Résumé

récupéré en direct d'OpenAlex

Purpose An engaged workforce has never been more important than it is now. Research continues to reveal a strong link between engaged employees and employee performance. Consequently, different strategies continue to be developed to enhance employee engagement (EE) in organisations. Unfortunately, many of these strategies have not worked due to the lack of trust that some employees may have towards organisational leaders. Thus, it is argued that the first step in building an effective EE is building trust, which will erode all sorts of suspicion of the intention of leaders in the organisation. Unfortunately, the literature is not clear about how to build such trust, especially in developing countries where the organisational environment is much different from that in developed ones; making the applicability of models in the developed world quite difficulty in these countries. How can public sector leaders build trust in the organisations in an environment where informality appears to be the norm? The purpose of this paper is therefore to ascertain how trust can be built in public organisations. Design/methodology/approach In order to answer the research questions, as well as obtain in-depth understanding of what is being done, the authors used the mixed methods approach in the data collection for the paper. In using mixed method data collection, the authors took both quantitative and qualitative approaches. Both qualitative and quantitative data were, however, collected concurrently. This was done for the sake of convenience, as there is little study on how to build trust or, even, EE in the Ghanaian context. The authors set out to explore these issues, and the only way for the authors to do so was to undertake the data collection simultaneously. Findings The paper examined critically four main areas to help leadership build trust: credibility, fairness, respect and communication. The study shows that both managers and employees firmly believe in building trust. Leaders were able to discuss the efforts they make to ensure that issues concerning trust building are addressed. At the same time, employees also agreed on the need to strengthen these variables. Practical implications The research identifies areas on which both leadership and employees can continually work to help bridge the gap between them if public organisations are to reap the benefits of EE. The authors are convinced that if the issues discussed here are addressed, and parties work on them, individuals will succeed in their own areas, but so will the organisations, which in turn will help in the development of he country. Originality/value From a theoretical perspective, it extends the work on EE, and offers new insight into this emerging concept from a developing countries perspective, where informality in the public sector is common. Most of the research on trust and EE has been either qualitative or quantitative in nature. Using the mixed methods approach means the authors will be explaining how both can help us better understand the “how” in building trust in the public sector. Thus, the paper is one of the few papers that have used the mixed methods approach to examine how trust can be built in public organisations.

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)0,009
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,546
Score d'incertitude au seuil0,955

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,335
Tête enseignante GPT0,420
Écart entre enseignants0,085 · 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'étudeQualitatif
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

Citations49
Publié2019
Routes d'admission1
Résumé présentoui

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