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Record W3008431962 · doi:10.1080/19368623.2020.1725954

Can employee workplace mindfulness counteract the indirect effects of customer incivility on proactive service performance through work engagement? A moderated mediation model

2020· article· en· W3008431962 on OpenAlexaff
Jichul Jang, WooMi Jo, Jinok Susanna Kim

Bibliographic record

VenueJournal of Hospitality Marketing & Management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIncivilityWork engagementMediationPerceived organizational supportEmployee engagementMindfulnessBusinessService (business)Structural equation modelingPsychologyWork (physics)MarketingPublic relationsSocial psychologyOrganizational commitmentComputer scienceSociologyEngineering

Abstract

fetched live from OpenAlex

Customer incivility is widespread in service industries, inevitable as employees offer service to a broad range of customers from diverse backgrounds. Stress caused by rude customers depletes front-line employees’ personal resources and develops to adverse work-related outcomes. Workplace mindfulness may provide resources to reduce the detrimental effect of customer incivility on work engagement. Furthermore, drawing from the conservation of resource theory(COR) and job demands-resouces (JD-R) model, this study investigates how work engagement mediates between customer incivility and employee proactive service performance among casino employees. The findings support that workplace mindfulness buffers the relationship between customer incivility and work engagement. Customer incivility is negatively associated with proactive service performance through work engagement. Theoretical and research implications and practical suggestions are proposed.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations114
Published2020
Admission routes1
Has abstractyes

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