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Record W2566024582 · doi:10.5539/ijef.v9n1p47

Antecedents of E-Banking Services by Customers for the Selected Commercial Banks in Sylhet, Bangladesh

2016· article· en· W2566024582 on OpenAlexvenueno aff
Mohammad Zahed Hossain

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsMobile bankingTelephone bankingBusinessRetail bankingSMS bankingThe InternetMarketingPopulationMobile phoneTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

<p>This study is conducted to identify customers view regarding cost effectiveness, time savings and security of different types of e-banking products like online banking, ATM banking, internet banking, mobile banking and telephone banking. E-banking is the alternative delivery channels that banks adopted for providing efficient banking services through the help of internet, computers, mobile phone etc. Banks’ customers were considered as population and primary data were collected through questionnaire. Descriptive statistics and Chi-square test were used for analyzing the data. The results indicated that customers prefer ATM banking services most, next to follow mobile banking and online banking. The customers believed that all types of e-banking products save time and except telephone banking others types of e-banking products were secured. Online banking and ATM banking services were not considered as cost effective. Analysis indicated no relationship between online banking and different demographic variables. ATM banking services was highly influenced by most of the demographic variables whereas internet banking, mobile banking and telephone banking influenced by few demographic variables i.e. age groups, education level, and monthly income. The results help banks to develop varieties of e-banking products and formulate strategies by considering the demographic characteristics of the customers. Customers expect more users friendly e-banking products along with diversify features and suggested to develop latest e-banking products like mobile apps based banking for ensuring long term customers relationship, attracting potential customers and keeping existing customers that may ensure consistent growth and profit as well.</p>

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.368

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 communication0.0000.000
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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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 designNot applicable
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

Citations5
Published2016
Admission routes1
Has abstractyes

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