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Record W24879113 · doi:10.1016/j.dcn.2014.04.003

Prophetic leadership and financial decision making quality: Partial Least Square (PLS) path modeling analysis.

2012· article· en· W24879113 on OpenAlexfundno aff
Nik Maheran Nik Muhammad, Shahram Akbarzadeh

Bibliographic record

VenueDevelopmental Cognitive Neuroscience · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchHarvard University
KeywordsAltruism (biology)FaithEthical leadershipSpiritualityServant leadershipSociologyPsychologyPublic relationsSocial psychologyManagementTransactional leadershipEpistemologyPolitical scienceEconomicsPhilosophy

Abstract

fetched live from OpenAlex

Prophetic leadership rests on the tenets of trait theory, spirituality, transcendent leadership and Religious leadership theory. It argues that leadership begins from within and works outward. Prophetic leadership also suggest the interconnectedness of leadership and social network theory, the necessity for finding common ground and synergy between leader and the followers and altruistic management to build leadership effectiveness. This paper proposed that prophetic leadership can be renewed by rethinking its theology and its epistemology across a full-spectrum of ‘Abrahamic faith’ prophets’ personality trait, organizational goal, task, and societal domains. It also analyzed the fitters of the model to financial sector via Partial Least Square (PLS) analysis using SmartPLS freely available software. Useable questionnaire of 110 respondents perceived that financial leaders hold the prophetic characteristics accept for posture and altruism. Financial decision qualities were derived from these characteristics.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.089
GPT teacher head0.293
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations5
Published2012
Admission routes1
Has abstractyes

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