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Record W2948150868 · doi:10.1108/mrr-12-2018-0494

The influence of fair supervision on employees’ emotional exhaustion and turnover intentions

2019· article· en· W2948150868 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Research Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBelongingnessPsychologyEmotional exhaustionSocial psychologyInteractional justiceOrganizational justiceDistributive justiceProcedural justicePerceived organizational supportPerceptionEmotional laborStructural equation modelingEconomic JusticeOrganizational commitmentBurnoutClinical psychology

Abstract

fetched live from OpenAlex

Purpose Despite the importance of interactional fairness, it has been assessed less frequently in literature than has procedural and distributive justice. The effects of interactional fairness are at times stronger than the effects for procedural and distributive fairness, given that supervisors are prominent in any workplace environment and the chief source for interpreting information related to matters such as suitable business practices and goals needed by organizations. This study aims to examine the mediating mechanisms through which interactional justice influences emotional exhaustion and turnover intentions. Specifically, the hypothesis proposes that perceived organizational support and a sense of belongingness simultaneously mediates the relationship between interactional justice and emotional exhaustion, which in turn affects withdrawal cognitions. Design/methodology/approach The author draws on the literature and studies on the link between organizational justice, stress and turnover to develop the hypotheses, collecting data from 141 employees of different organizations and occupations. Findings Results of partial least squares structural equation modeling and Preacher and Hayes’ (2004) bootstrapping approach reveal that interactional justice is significantly positively associated with perceptions of organizational support and belongingness, which in turn is negatively associated with emotional exhaustion. Research limitations/implications Interactionally fair treatment engenders perceptions of organizational support and heightens a sense of belongingness, subsequently reducing the burden of physical and emotional fatigue on individuals and thereby freeing employees from engaging in turnover cognitions. Practical implications The study underscores the importance of fair supervisors. Results suggest that fair supervisors help employees estimate the extent to which their organization is supportive. In addition, fair supervisors reassure subordinates that they are valued, which in turn lessens the experience of emotional exhaustion, giving organizations a competitive advantage due to the more favorable behavioral intentions held by employees. Originality/value Interactional justice has been assessed less frequently in literature than has procedural and distributive justice. Research has overlooked the underlying process of how interactional justice reactions might motivate emotional exhaustion and turnover intentions responses. Thus, this study identifies an expanded group of mediators that link interactional justice to emotional exhaustion and turnover intentions.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.049
GPT teacher head0.332
Teacher spread0.282 · 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