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Record W2973422854 · doi:10.1177/0972150919861783

Organizational Response to Goods Failure Complaints: The Role of Culture on Perceptions of Interactional Justice and Customer Satisfaction

2019· article· en· W2973422854 on OpenAlexaffabout
Etayankara Muralidharan, Wenxia Guo, Hesham Fazel, William Wei

Bibliographic record

VenueGlobal Business Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsAcadia UniversityMacEwan University
Fundersnot available
KeywordsComplaintInteractional justiceService recoveryEconomic JusticeCustomer satisfactionCollectivismContext (archaeology)BusinessPerceptionPsychologySocial psychologyValue (mathematics)Service (business)MarketingService qualityOrganizational justiceOrganizational commitmentEconomicsPolitical scienceIndividualismMicroeconomics

Abstract

fetched live from OpenAlex

It is well recognized that in a service failure context, cultural value orientations interact with firm responses to service failures to influence perceptions of fairness (justice) and satisfaction. We examine whether this effect is applicable in the case of goods failure complaint context. Using an experimental design with data from Hong Kong and Canada, we investigate how customer evaluations of firm responses are influenced by interplay of consumers’ value orientation and nature of firm responses to the goods failure complaint [whether complaint resolution is initiated by the firm (vs. initiated by the customer), customer is informed about the progress of complaint resolution (vs. not informed about the progress)]. Our findings reveal that the cultural values of collectivism and uncertainty avoidance do interact with the nature of firm’s response to influence perception of interactional justice. Finally, interactional justice positively impacts overall complaint resolution satisfaction.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.271
Teacher spread0.261 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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