MétaCan
Menu
Back to cohort
Record W4386163180 · doi:10.5539/ibr.v16n9p68

The Competent Leaders of the Saudi Non-Profit Organizations

2023· article· en· W4386163180 on OpenAlexvenueno aff
Rola Younis Masoud Mohammed, Muhammed Zafar Yaqub

Bibliographic record

VenueInternational Business Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsBusinessSustainabilityQualitative researchFocus groupMarketingSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

To attain Saudi Arabia’s Vision 2030’s goal of developing leadership, we need to understand the key issues that have emerged in the context of learning skills relating to leadership in non-profit organizations (NPOs) to enhance their efficacy for management positions. This study elaborates upon the relevant abilities of leaders of NPOs. A qualitative approach involving interviews with 15 directors from renowned non-profit Saudi organizations was used. These 15 leaders, together with 6 other employees from NPOs participating in a focus group, were the primary informants. The study involved semi-structured interviews with professionals from non-profit sectors such as education, health, environmental sustainability, and human resources to summarize critical elements that either help facilitate or negatively affect these individuals’ ability to contribute to institutional results. The results revealed six competency groups – namely personal, management, social, industry-specific, work-specific, and academic – which demonstrated the usefulness of the approach in gaining useful insights that would otherwise not have emerged. The results indicate that leaders of NPOs should exhibit these six levels of competencies to counter internal and external difficulties, and to effectively contribute to achieving organizational goals.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.110
GPT teacher head0.427
Teacher spread0.317 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicSocioeconomic Development in MENAFrench-language works237,207