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Record W3164451478 · doi:10.1111/bjir.12610

Customer aggression, employee voice and quit rates: Evidence from the frontline service workforce

2021· article· en· W3164451478 on OpenAlexaffabout
Xiangmin Liu, Danielle D. van Jaarsveld, Yoshio Yanadori

Bibliographic record

VenueBritish Journal of Industrial Relations · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAggressionWorkforceTurnoverBusinessSample (material)Service (business)Control (management)Customer servicePsychologyWork (physics)Demographic economicsPublic relationsSocial psychologyMarketingPolitical scienceManagementEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

Abstract In this study, we examine how establishment‐level aggression originating from customers can lead to voluntary turnover. We also examine whether establishment‐level factors, such as collective voice, high involvement work practices and control‐based work practices, moderate this relationship. By analysing a sample of 139 call centres in Canada, we found that establishment‐level customer aggression is positively related to the workforce quit rate. Furthermore, we found that this positive relationship is weaker in establishments where employees have access to collective voice and in establishments that use fewer control‐based human resource practices.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.153
GPT teacher head0.403
Teacher spread0.249 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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