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Record W4414759683 · doi:10.20429/amtp.2023.37

Service Failure and Recovery: The Role of Customer Forgiveness and Perceived Justice in Customers’ Coping Behaviors

2023· article· en· W4414759683 on OpenAlexaff
Andreawan Honora, Kaiyu Wang, Wen‐Hai Chih

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsBrock UniversityWestern University
Fundersnot available
KeywordsForgivenessInteractional justiceService recoveryPerceptionDistributive justiceCustomer serviceService qualityEconomic Justice

Abstract

fetched live from OpenAlex

This research investigated the role of customer forgiveness as the underlying mechanism of the effect of service failure severity on customers’ coping behaviors. It also investigated the moderating role of customers’ justice perceptions in the proposed model. The findings showed that customer forgiveness is essential in mending the relationships and lowering customer avoidance. Customer forgiveness was less negatively affected by service failure severity when customer perceived service providers’ recovery efforts with higher levels of justice. Additionally, this research explored the moderating effects of three dimensions of justice on the relationship between service failure severity and customer forgiveness. The findings demonstrated that the higher levels of distributive justice weakened the negative effect of service failure severity on customer forgiveness, especially when customer perceived lower levels of interactional justice. However, such effect was lessened when customer perceived higher levels of interactional justice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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