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Record W4414657843 · doi:10.1002/mar.70058

Stories of Service Slip‐Ups: Judgments of Pre‐Service Deservingness Shape Reactions to Service Failures

2025· article· en· W4414657843 on OpenAlexaff
Bijit Ghosh

Bibliographic record

VenuePsychology and Marketing · 2025
Typearticle
Languageen
FieldComputer Science
TopicSentiment Analysis and Opinion Mining
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlameService (business)PerceptionSkepticismEconomic JusticeService providerAttribution

Abstract

fetched live from OpenAlex

ABSTRACT Consumers frequently share service failures, often leading to brand avoidance. Existing research focuses on service‐related factors that emerge during or after the encounter, leaving a critical gap in understanding how pre‐service factors influence reactions. This study reveals that consumer actions before the service—despite being entirely unrelated to the failure—significantly influence third‐party responses. Across four studies, including a consequential choice‐based experiment, it is shown that consumers perceived as undeserving of the service do not trigger typical negative reactions when they complain. Interestingly, these deservingness perceptions, formed before the service encounter and based on factors unrelated to it, drive a shift in third‐party reactions. Moreover, third parties shift the blame onto the complainant and actively support the brand due to justice‐restorative motives instead of just dismissing the complaint. Theoretically, this study introduces pre‐service deservingness as a previously unexplored factor, demonstrating that fairness heuristics shape marketplace behavior even when events are unrelated and temporally distinct. Additionally, it uncovers a shift from skepticism to active brand advocacy, where third parties intervene to restore justice rather than merely discount complaints. From a managerial perspective, brands can strategically navigate service failures by leveraging third‐party justice perceptions to influence consumer advocacy and brand defense.

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 score0.477
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.341
Teacher spread0.311 · 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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