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Record W2969365814 · doi:10.17722/ijme.v13i2.1109

The Evaluation of Leadership Development at a State Owned Enterprise in South Africa

2019· article· en· W2969365814 on OpenAlexvenueno aff
Krishna Govender, Miston Mapuranga

Bibliographic record

VenueInternational Journal of Management Excellence · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetLeadership styleAutocracyPublic relationsLeadership developmentTransactional leadershipEmployee engagementLeadership studiesBusinessCLARITYPolitical scienceDemocracy

Abstract

fetched live from OpenAlex

The study was sparked by concerns in the Human Resources Department at Denel, a State Owned Company/Enterprise in South Africa, regarding the state of leadership in the organization. The concerns were primarily that the leadership style in general, was ‘command and control’ - autocratic, bureaucratic and lacking the necessary commercial mindset and emotional intelligence needed to deal with employees from a motivational and employee-engagement perspective. The purpose of the research was to conduct an investigation into leadership at Denel and to analyse the perceptions, opinions and concerns of all stakeholders in the company. A qualitative research methodology was used and the findings confirmed that leadership styles at Denel were indeed traditional command and control, autocratic, lacked a commercial mindset and lacked emotional intelligence. Furthermore, the existing repertoire of leadership development programmes lacked work-based application relevance and the leadership development approaches were haphazard, with no proper focus and direction. Furthermore, there was no measurement of the impact of the leadership development interventions in the company to determine the return on investment. The recommendation is that leaders at Denel should create a culture of talent optimization, be transformed into business leaders and ensure employee motivation and engagement levels are enhanced within the company.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.259
Teacher spread0.200 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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