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Record W2025499409 · doi:10.5539/ibr.v7n11p203

The Impact of Complaints' Handling on Customers' Satisfaction: Empirical Study on Commercial Banks' Clients in Jordan

2014· article· en· W2025499409 on OpenAlexvenueno aff
Mohammad Z. Shammout, Shafig Al-Haddad

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComplaintBusinessCustomer satisfactionService qualityService (business)Sample (material)MarketingService recoveryQuality (philosophy)VariablesOrder (exchange)Value (mathematics)Operations managementFinanceEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

This study aims at identifying the most important impacts of complaints' handling on customers' satisfaction in the commercial banks' in Jordan, also aims to provide recommendations and suggestions to the top managements to handle customers' complaints in order to enhance customers' satisfaction. The sample of the study consists of five commercial banks in Jordan (Housing Bank, Arab Bank, Bank of Jordan, Cairo Amman Bank And Ahli Bank) of 419 questionnaires were distributed in several phases until 384 questionnaires have been confirmed. Complaints' handling was the main domain of the study as the dependent variable and consists of six dimensions which are considered to be the sub independent variables for the purpose of the study (service recovery, service quality, switching cost, service failure, service guarantee and perceived value) and the dependent variable was customer satisfaction. The results of the research showed that there is a statistically significant impact of the overall dimensions of complaint handling (service recovery, service quality, switching cost, service failure, service guarantee, and perceived value) on customer 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 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)0.002
metaresearch head score (Gemma)0.001
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 score0.046
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.074
GPT teacher head0.406
Teacher spread0.332 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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