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Record W2970564154 · doi:10.5539/ibr.v12n9p62

The Effect of Using Mobile Banking Services Applications on Electronic Word of Mouth: The Mediating Role of Perceived Trust

2019· article· en· W2970564154 on OpenAlexvenueno aff
Lama Zalloum, Hamad Rashid Al-ghadeer, Nawras M. Nusairat

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMobile bankingPersonalizationBusinessMediationUsabilityService qualityInformation qualityQuality (philosophy)Descriptive statisticsService (business)MarketingAdvertisingComputer scienceInformation systemStatisticsEngineering

Abstract

fetched live from OpenAlex

The objective of this study is to examine selected dimensions of mobile banking, (such as Ease of Navigation, Personalization Level, Information Quality, Rewards) on e-wom through the mediating role of perceived trust. Self-structured questionnaire is used to collect data which is then shared via Google forms online and targeted only to the users of mobile banking services application in Jordan. Quantitative and analytical methods were used to analyze the data. 499 questioners were returned, 30 of which were rejected as they were not using mobile banking service applications. 469 of the questionnaires were accepted and analyzed using reliability test analysis, descriptive statistics and regression process. The findings of the study indicate that there is a statistically significant effect of mobile banking (Ease of Navigation, Personalization Level, Information quality, Rewards) through the mediation role of perceived trust on relationships between using mobile banking services and e-wom. It is recommended that banks pay more attention to mobile banking and build powerful and good E-WOM in order to spread the use of mobile banking rapidly.

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 score0.247
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.309
Teacher spread0.297 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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