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Record W4224882722 · doi:10.33423/jop.v22i1.5118

Effectiveness of Servant Leadership in an Organizational Environment: Influence on Human Capital and Performance in Social Entrepreneurship

2022· article· en· W4224882722 on OpenAlexaff
Victor Mignenan, Imen Latrous, Brahim Meddeb

Bibliographic record

VenueJournal of Organizational Psychology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsServant leadershipExcellenceEntrepreneurshipPassionHuman capitalPublic relationsSocial capitalCredibilityAgile software developmentManagementSociologyBusinessPolitical scienceLeadership stylePsychologyEconomic growthSocial psychologyEconomicsSocial scienceFinance

Abstract

fetched live from OpenAlex

Studies have demonstrated the effectiveness of servant leadership on human capital and social enterprise are few. To increase knowledge, research with the Network of Youth Organizations (ROJ) in Chad was carried out. The hybrid approach has been deployed. The results showed that the credibility and human warmth of the servant leader promote the adherence and commitment of employees in the process of social entrepreneurship. Then, the passion for the excellence of the servant leaders is a real lever for the performance of social entrepreneurship. However, the passion for excellence contributes little to social entrepreneurship. But it is above all agile, autonomous and committed human capital that increases performance in social entrepreneurship. The article is useful for scholars who will find a renewed definition of servant leadership. While public actors will find new avenues of solutions to suggest policies and programs to strengthen the capacity of the OR. OR leaders will find strategies to effectively enhance and enhance their human capital.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.046
GPT teacher head0.275
Teacher spread0.229 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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