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Customer-Directed Sabotage and Its Relationships with Employee Motives and Sense of Power

2025· article· en· W4416007564 on OpenAlexaff
Su Kyung Kim, Yujie Zhan

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsWilfrid Laurier UniversityUniversity of Manitoba
Fundersnot available
KeywordsConceptualizationModerationOutcome (game theory)Power (physics)Sample (material)Dimension (graph theory)Service (business)

Abstract

fetched live from OpenAlex

Customer-directed sabotage refers to employees’ counterproductive work behavior that negatively affects service and harms customers’ legitimate interests (Wang et al., 2011). While it can manifest in many different behaviors, such as a flight attendant yelling at a passenger “Shut up! I’m not your servant” (Land, 2022) and a call center agent intentionally putting a customer on hold for a long period of time (Skarlicki et al., 2008), customer-directed sabotage has been conceptualized and studied as one broad concept. We consider a subtle-obvious dimension in the conceptualization and measurement of customer-directed sabotage. We contend that doing so enables a more precise examination of customer-directed sabotage and its relationship with employee motives and outcomes. Using a sample of 300 service employees, we conducted a critical incident technique-based survey to gather customer-directed sabotage events and uncover service employee motives for and outcome of customer-directed sabotage. We explored the relationship between employee motives (i.e., employee boredom, customer mistreatment) and customer-directed sabotage as well as the relationship between customer-directed sabotage and employee outcome (i.e., sense of power). Also, given the power dynamic inherent in employee–customer relationship, we examined employee chronic powerlessness as a potential moderator to the relationship between customer-directed sabotage and sense of power. We discuss implications of our findings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.396

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.327
Teacher spread0.297 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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