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Record W4297474935 · doi:10.1108/ijcma-04-2022-0074

Conflict with customers: the limits of social support and job autonomy in preventing burnout among customer service workers

2022· article· en· W4297474935 on OpenAlexaff
Alyssa T. Klingbyle, Greg A. Chung‐Yan

Bibliographic record

VenueInternational Journal of Conflict Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAutonomyBurnoutPsychologyContext (archaeology)Social supportService (business)Job designSocial psychologyBusinessJob performanceMarketingJob satisfactionPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine the burnout of workers in customer service roles as a result of conflict with customers; and the role that coworker support, non-work-related social support and job autonomy play in buffering customer service workers from conflict with customers. Design/methodology/approach A sample of 191 young customer service workers completed an online self-report questionnaire. Findings Although it was found that coworker support, non-work-related social support and job autonomy moderated the relationship between customer conflict and burnout, the form of the interactions was not as expected. Rather than buffering customer service workers specifically against customer conflict, it was found that as customer conflict intensifies, it gradually erodes the positive benefits that coworker support, general social support and job autonomy have in preventing burnout as a result of general work stress. Originality/value This study is one of few to empirically investigate the unique stressors experienced by customer service workers. It also expands understanding of social support and job autonomy in the context of work stress, demonstrating that there are limits to the effectiveness of these personal and organizational resources in preserving worker well-being.

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)0.000
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 score0.334
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.260
Teacher spread0.244 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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