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Record W3015987983 · doi:10.19173/irrodl.v21i2.4578

An Empirical Study on Service Recovery Satisfaction in an Open and Distance Learning Higher Education Institution in Malaysia

2020· article· en· W3015987983 on OpenAlexvenueno aff
Mohd Rushidi bin Mohd Amin, Shishi Kumar Piaralal, Yon Rosli Daud, Baderisang Mohamed

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsService recoveryProcedural justiceDistributive justicePsychologyCustomer satisfactionLoyaltyInterpersonal communicationInteractional justiceService (business)Structural equation modelingEconomic JusticeSocial psychologyApplied psychologyService qualityMarketingBusinessOrganizational commitmentOrganizational justicePolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study investigated the relationships among justice dimensions (distributive, procedural, interpersonal, and informational), university image, service recovery satisfaction, and customer behavioural outcomes (trust, word of mouth, repurchase intention, and loyalty). This study adopted a cross-sectional survey approach and data were collected through a survey of 303 students of Open University Malaysia in Malaysia who experienced service failure and service recovery. The framework was tested via partial least square structural equation modelling, and the results revealed a significant relationship between justice dimensions and service recovery satisfaction in terms of procedural and interpersonal justice. Service recovery satisfaction had a significant effect on all customer behavioural outcomes investigated. University image did not have a moderating effect on the relationship between justice dimensions and service recovery satisfaction. Theoretical and practical implications of the study are discussed in this paper.

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)0.004
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 score0.018
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.155
GPT teacher head0.460
Teacher spread0.305 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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