MétaCan
Menu
Back to cohort
Record W3010973466 · doi:10.5267/j.ijdns.2019.12.004

The effects of factors influencing on user behavior intention to use mobile payment: Evidence from Cambodia

2020· article· en· W3010973466 on OpenAlexvenueno aff
Nam Hung, Jacquline Tham, S. M. Ferdous Azam, Abdol Ali Khatib

Bibliographic record

VenueInternational Journal of Data and Network Science · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsMobile paymentPaymentBusinessInternet privacyComputer science

Abstract

fetched live from OpenAlex

Mobile payment is considered as a technology innovation which is being exploited and further expanded in both developed and developing or emerging countries. It is considered as an alternative method to cash payment method. This study is employed with the objective of exploring how Cambodian user behavior intention is affected by perceived transaction convenience, perceived transaction speed, social influences, and facilitating condition. Each factor is measured by different items and there are 38 items developed in this study. The data is collected by distributing questionnaires to Cambodian users and 329 questionnaires are collected successfully.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.047
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.001
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 head0.145
GPT teacher head0.411
Teacher spread0.266 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Data and Network ScienceSame topicTechnology Adoption and User BehaviourFrench-language works237,207