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Record W3039465913 · doi:10.5267/j.msl.2020.6.042

Engaging employees through spiritual leadership

2020· article· en· W3039465913 on OpenAlexvenueno aff
William D. Hunsaker, Woojin Jeong

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyBusinessPublic relationsSocial psychologyMarketingPolitical science

Abstract

fetched live from OpenAlex

This study explores how spiritual leadership through its effects on individuals' spiritual well-being enhances employee engagement and augments employees' organizational commitment. The research conducted a general survey of 207 millennial employees in China, which was tested using partial least squares (PLS) structural equation modeling. The results suggest that employee engagement was significantly influenced by spiritual leadership. In addition, the results demonstrate that organizational commitment was influenced by the sequential intervening effects of employee wellbeing and work engagement. Both the explained variance (R 2 ) and the effect size of exogenous variables on endogenous variables ( 2 ) showed moderate-to-high effects, supporting the predictive ability of this PLS spiritual leadership model. This study suggests that young workers' engagement and commitment to their work and organizations are roused when the organization and its leaders satisfy workers' higher-order humanistic needs. This is accomplished when employees' socio-psychological resources, such as spiritual well-being, are enhanced through spiritual leadership, which can be augmented by organizational climates that embrace young workers' "whole" selves. This study expands our understanding of the role that spiritual leadership plays in not only motivating organizational commitment but, more importantly, in engaging employees in their work roles.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.003
Scholarly communication0.0010.001
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 head0.166
GPT teacher head0.329
Teacher spread0.163 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2020
Admission routes1
Has abstractyes

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