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Record 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 on OpenAlexaff
Frank L. K. Ohemeng, Theresa Obuobisa‐Darko, Emelia Amoako Asiedu

Bibliographic record

VenueInternational Journal of Public Leadership · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsPublic relationsPublic sectorWorkforceBureaucracyQualitative propertyContext (archaeology)Data collectionEmployee engagementNorm (philosophy)Order (exchange)Developing countryBusinessPolitical scienceSociologyPoliticsComputer scienceEconomic growthEconomics

Abstract

fetched live from 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.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.335
GPT teacher head0.420
Teacher spread0.085 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations49
Published2019
Admission routes1
Has abstractyes

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