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Record W2804499275 · doi:10.5539/ibr.v11n6p102

The Impact of Electronic Banking Services on Customer Satisfaction in the Sudanese Banking Sector

2018· article· en· W2804499275 on OpenAlexvenueno aff
Adam Ahmed Musa Hamid, Nabil Mohamed Alabsy, Mohanad Abbas Mukhtar

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsElectronic bankingBusinessCustomer satisfactionThe InternetMarketingRetail bankingBanking industryQuality (philosophy)AccountingComputer science

Abstract

fetched live from OpenAlex

This research paper aims to study the impact of electronic banking services on customer satisfaction at Sudanese banks. Questionnaires were designed by the researchers. Data and information have been collected and analyzed from the internet users in the Sudanese banks clients. The study found that there are statistical significant differences of electronic services provided by the Sudanese banks on customer satisfaction. The study attempted to explain the various means of electronic banking services which might lead to the customer satisfaction.This paper showed that the banking services over the internet has a positive impact on customer satisfaction. This study recommended that the bank management should focus on spreading the knowledge of the electronic banking services to the customers. This study emphasized the importance of the electronic banking services and recommended that the bank management should spread the technological awareness among current and prospective customers, and develop suitable infrastructure for electronic banking services in the Sudanese banking sector.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.484
Teacher spread0.347 · 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.

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

Citations16
Published2018
Admission routes1
Has abstractyes

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