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Record W3195634630 · doi:10.3390/su13179608

The Role of Ethical Leadership in Managing Occupational Stress to Promote Innovative Work Behaviour: A Cross-Cultural Management Perspective

2021· article· en· W3195634630 on OpenAlexaffabout
Adnan ul Haque, Fred A. Yamoah

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsYorkville University
Fundersnot available
KeywordsWorkforceEthical leadershipLeadership styleStatus quoPublic relationsPsychologyContext (archaeology)Perspective (graphical)Occupational stressSociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This study examines the role of ethical leadership in managing occupational stress to engender innovative work behaviour (IWB) in cargo logistic SMEs in a contrasting cross-cultural management context of Canada and Pakistan. We draw on Trait Activation Theory to develop the conceptual and theoretical framework of the study. Using connections and a networking approach, a proportionate equal sample of nine SMEs were selected for the study. Analysis of the data from the semi-structured Skype and face-to-face interviews with 38 supervisors and 97 employees showed that ethical leadership plays a vital role in reducing occupational stress and increasing employees’ IWB in both countries. Employees in both countries perceiving ethical leadership exhibit more creative-constructive behaviour. The results further demonstrate that males relative to females in both countries have a higher tendency of exhibiting risk-taking behaviour and IWB, resulting from leaders’ support. Similarly, males have higher tendency of challenging the prevailing “status quo” within the organisations than females. Generally, the Pakistani workforce scored higher in contrast to the Canadian workforce in demonstrating IWB due to ethical leadership support, despite higher perception of occupational stress. Cross-cultural management implications are duly outlined.

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)0.001
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 score0.253
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.321
Teacher spread0.292 · 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

Citations62
Published2021
Admission routes2
Has abstractyes

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