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Record W2898861492 · doi:10.5539/mas.v12n11p169

Impact of Strategic Leadership Competencies on Enhancing Core Competencies in Organizations: Applied Study on AlManaseer Group for Industrial & Trading

2018· article· en· W2898861492 on OpenAlexvenueno aff
Hani J. Irtaimeh

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsCore competencyStrategic leadershipStrategic thinkingBusinessCore (optical fiber)Knowledge managementStrategic planningMarketingEngineeringComputer science

Abstract

fetched live from OpenAlex

The aim of this study is to explore the impact of Strategic leadership competencies dimensions (Strategic Thinking Competencies, Leadership Competencies) on core competency in AlManaseer Group for Industrial & Trading. For this purpose, a questionnaire was developed to collect data from the study population which consists of 180 leaders. This is aimed at testing the hypotheses and achieving the objectives of the study. The most important results that the study achieved were that there was a statistically significant impact of Strategic leadership competencies with its dimensions (Strategic thinking competencies, leadership competencies) on core competency. The most important recommendations of the study were the necessity of AlManaseer Group should evaluate the core competencies; periodically and continuously in order to rebuild the critical resources which are the pillars of core competencies and in line with the requirements and conditions of strategic thinking and leadership competencies.

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)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.207
GPT teacher head0.303
Teacher spread0.096 · 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
Published2018
Admission routes1
Has abstractyes

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