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Record W2968001313 · doi:10.5430/ijfr.v10n6p54

Customer Satisfaction as Intervening Between Use Automatic Teller Machine (ATM), Internet Banking and Quality of Loyalty (Case in Indonesia)

2019· article· en· W2968001313 on OpenAlexvenueno aff
Indrayani Indrayani, Chablullah Wibisono, Sanni Aritra, Iskandar Muda

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer satisfactionService qualityLoyalty business modelVariablesBusinessVariable (mathematics)Regression analysisMarketingThe InternetLoyaltyQuality (philosophy)Service (business)StatisticsAdvertisingComputer scienceMathematicsWorld Wide Web

Abstract

fetched live from OpenAlex

This research aims to determine the effect of customer satisfaction as an intervening variable between utilization of Automatic Teller Machine (ATM) variable, Internet Banking variable and service quality variable to variable customer loyalty at PT. Bank Mandiri Tbk in Batam city, Indonesia. This research is collecting the data in the form of primary and secondary data. Collecting the data in this study using questionnaires in the form of respondents Involved in this research were 187 customers. The number is obtained by using the formula Slovin with random sampling. The collected the data were processed and Analyzed by using SPSS and SEM for normality, regression coefficient, and determination. From the result show that the variable utilization ATM, Internet Banking and service quality affect to customer satisfaction has 83%. That is the service quality variables of the most dominant influence on customer satisfaction and customer loyalty.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.441
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2019
Admission routes1
Has abstractyes

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