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Record W2973564568 · doi:10.1080/10696679.2019.1644958

I Can Forgive You, But I Can’t Forgive the Firm: An Examination of Service Failures in the Sharing Economy

2019· article· en· W2973564568 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Marketing Theory and Practice · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsForgivenessAttributionEmpathyBusinessService (business)Service providerCompensation (psychology)Principal–agent problemSocial psychologyPsychologyMarketingFinance

Abstract

fetched live from OpenAlex

Despite rapid growth of the sharing economy, little is known about consumers’ reactions when sharing services fail. Drawing on attribution theory, in three studies we show that consumers forgive such service failures varyingly, depending on the controllability and the locus of attribution of the failures. Specifically, when a failure has low controllability, consumers are more forgiving when it is attributed to an individual service provider than when it is attributed to a service enabling organization. Empathy toward the service provider explains the increased forgiveness. However, no difference in forgiveness is observed in the case of highly controllable failures, irrespective of the source of attribution. Furthermore, the effect of two recovery strategies – compensation and apology – also varies depending on these conditions. Theoretical and managerial implications are discussed.

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.

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)0.070
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0700.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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