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Record W2337220158 · doi:10.4172/1204-5357.1000117

Service Quality Dimensions and Customer Satisfaction with Online Services of Nigerian Banks

2015· article· en· W2337220158 on OpenAlexvenueno aff
Okeke TC, Ezeh GA, Ugochukwu NOA

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleService qualityCustomer satisfactionReliability (semiconductor)Service (business)Quality (philosophy)MarketingSample (material)BusinessVariablesCustomer Service AssuranceScale (ratio)Regression analysisComputer scienceCustomer retentionStatisticsMathematics

Abstract

fetched live from OpenAlex

This study concerns the relationship between service quality dimensions and customer satisfaction with online/ebanking services of Nigerian banks. Seven service dimensions were included in the study and they are: reliability, assurance, responsiveness, perceived risk, tangibility, security, and price. The study was based on a sample 400 respondents out of which 258 responded to the questionnaire. The seven service quality variables and the dependent variable were all measured with a number of items each using seven-point Likert scale. The analysis was conducted with Multiple Linear Regression analysis (MLR) and the results show that five out of the seven variables: price, security, perceived risk, responsiveness and assurance are significant in enhancing customer satisfaction with online services of Nigerian banks. The other two variables: reliability and tangibility are not significant and require further exploration. The study provides necessary input for bank management to increase customers’ involvement through improving service quality; lowering risk; and enhancing security of operations. Policy implications were highlighted

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)0.001
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 score0.055
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.278
Teacher spread0.236 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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