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Record W2995087108 · doi:10.1002/job.2425

“Killing them with kindness”? A study of service employees' responses to uncivil customers

2019· article· en· W2995087108 on OpenAlexafffund
Kirsten Robertson, Jane O’Reilly

Bibliographic record

VenueJournal of Organizational Behavior · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of OttawaUniversity of the Fraser Valley
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIncivilityTypologyIntrapersonal communicationInterpersonal communicationSocial psychologyPsychologyService (business)KindnessPerspective (graphical)Public relationsBusinessMarketingSociologyPolitical science

Abstract

fetched live from OpenAlex

Summary Experiencing uncivil customers is a frequent reality for many people working in the service industry. Past research has established that dealing with uncivil customers can be distressing for employees and can sometimes lead them to engage in reciprocal, discourteous behavior. The purpose of our research is to delve deeper into the experience of customer incivility from the perspective of service employees in order to better understand the various ways in which they respond to customer incivility. We conducted 64 interviews with service employees across an array of occupations and developed a typology of responses to customer incivility. These responses fell into four categories based on the extent to which service employees' actions were intended to promote social harmony (and therefore could broadly be considered civil or uncivil), as well as their perceived agency in the situation. We describe how each response was associated with different interpersonal and intrapersonal consequences and explain the implications of our typology for management theory and practice.

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 categoriesInsufficient 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 score0.220
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)0.0010.000

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 head0.030
GPT teacher head0.308
Teacher spread0.279 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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