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Record W3091535573 · doi:10.5267/j.msl.2020.8.037

Nexus between idiosyncratic deals and work engagement via psychological empowerment: A PLS-SEM approach

2020· article· en· W3091535573 on OpenAlexvenueno aff
Muhammad Shahid Shams, Tang Swee Mei, Zurina Adnan

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Work engagementWork (physics)EmpowermentPsychologyStructural equation modelingSocial psychologyKnowledge managementApplied psychologyComputer scienceBusinessPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Idiosyncratic deals have been used by many organizations to achieve desirable employees' behavior and work-related positive outcomes. Drawing upon the social exchange theory, this study aims at investigating the connections between idiosyncratic deals and work engagement. Besides, this study also scrutinizes the mediating role of psychological empowerment in the relationship between idiosyncratic deals and work engagement. This study applied smart PLS-SEM v.3.2 for the data analysis to ascertain the relationship between the study variables. Using an online survey, data are collected from 310 academicians working in the public higher education institutions of Pakistan. The finding of this study shows a significant positive relationship between idiosyncratic deals and work engagement. Furthermore, finding of the study also divulges that psychological empowerment mediates the relationship between idiosyncratic deals and work engagement. Hence the management and the policy makers in the public higher education institutions should focus on the provision of idiosyncratic deals based on the personal and professional needs of academicians to strengthen their feeling of psychological empowerment which subsequently results in fostering their engagement at work.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.041
GPT teacher head0.252
Teacher spread0.211 · 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.

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

Citations14
Published2020
Admission routes1
Has abstractyes

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