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Record W3095555403 · doi:10.5267/j.msl.2020.10.022

Impact of employee empowerment on organizational commitment through job satisfaction in four and five stars hotel industry

2020· article· en· W3095555403 on OpenAlexvenueno aff
Nasser S. Al-Kahtani, Shahid Iqbal, Mariam Sohail, Faisal Sheraz, Sarwat Jahan, Bilal Anwar, Syed Arslan Haider

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational commitmentJob satisfactionEmpowermentAffective events theorySocial exchange theoryPsychologyTest (biology)Structural equation modelingSocial psychologyBusinessMarketingBusiness administrationJob performanceJob attitudeMathematicsStatisticsPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to empirically test the impact of employee empowerment on organizational commitment through the mediating role of job satisfaction. The non-probability random sampling technique and time lag was used to collect data from 307 employees working at four and five Stars Hotels in two cities Rawalpindi, Islamabad of Pakistan. Smart Partial least squares-structural equation modeling (Smart PLS SEM v.3.2.8) was used to test the hypotheses. The result indicates that employee empowerment has a significant and positive impact on organizational commitment. Also, job satisfaction is considered as a potential mediator between employee empowerment and organizational commitment. Furthermore, to sup-port the results current study used the social exchange theory. Finally, some theoretical and practical contributions to employee empowerment and organizational commitment literature, and research limitations and future directions are presented.

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)0.001
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.253
Teacher spread0.234 · 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

Citations97
Published2020
Admission routes1
Has abstractyes

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