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Record W2031588937 · doi:10.5539/ijms.v6n2p58

Exploring Customer Dissatisfaction/Satisfaction and Complaining Responses among Bank Customers in Ghana

2014· article· en· W2031588937 on OpenAlexvenueno aff
Simon Gyasi Nimako, Anthony Freeman Mensah

Bibliographic record

VenueInternational Journal of Marketing Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer satisfactionBusinessMarketingBanking industryAdvertisingFinance

Abstract

fetched live from OpenAlex

The paper explores the relationship between customer dissatisfaction/satisfaction and complaining responses among bank customers in Ghana banking industry. The study was a cross-sectional survey that used a self-administered structured questionnaire to collect primary data from 448 customers from ten selected banks in Ghana. The findings are that, though dissatisfaction causes customer complaining, dissatisfaction was more prevalent among non-complainers than complainers. Again, frequency of complaining is more likely to increase overall satisfaction if managed effectively. The most likely to be used complaining responses are complaining in person and refraining from using the bank’s services, while the least likely to be used complaining responses are complaining to the mass media and consumer associations. Moreover, public bank customers are more likely to complain by refraining from using the bank’s services and warning family and friends than private bank customers do. There was significant correlation between complaining responses and frequency of complaining, and between complaining responses and overall satisfaction. Theoretical and managerial implications are discussed. In spite of its limitations, the paper contributes to the body of knowledge in the area of consumer complaining behaviour in banking industry in emerging economies.

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)0.003
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 score0.055
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.082
GPT teacher head0.309
Teacher spread0.227 · 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

Citations21
Published2014
Admission routes1
Has abstractyes

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