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Record W4296111985 · doi:10.51327/nnbj8521

Instigated Incivility, Guilt Expression, and Performance: Moderating Role of Religiosity

2022· article· en· W4296111985 on OpenAlexaff
Inam Ul Haq, Dirk De Clercq, Muhammad Umer Azeem

Bibliographic record

VenueJournal of Management Spirituality & Religion · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsBrock University
Fundersnot available
KeywordsIncivilityReligiositySocial psychologyFaithPsychologyFeelingExpression (computer science)Epistemology

Abstract

fetched live from OpenAlex

With a theoretical grounding in conservation of resources theory, this study examines how instigated incivility may boost instigators' job performance, in a process that might be explained by the instigators' expressions of guilt and moderated by their religious faith. The hypotheses tests rely on multisource, three-wave data collected from employees and their supervisors in Pakistani organizations. The findings, generated with the Process macro, affirm that (1) an important reason that instigated incivility translates into enhanced in-role and extra-role job performance is the instigators' desire to express guilty feelings and (2) this mediating role is especially prominent among employees who hold strong religious beliefs. For management scholars, this study's focus on incivility perpetrators provides an important complement to traditional considerations of incivility victims. For practitioners, it reveals how employees' own uncivil behaviors, somewhat counterintuitively, lead to enhanced performance outcomes and how religious faith serves as a catalyst of this process.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.017
GPT teacher head0.279
Teacher spread0.261 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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