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Record W3198407226 · doi:10.5267/j.ijdns.2021.7.007

Adoption of e-payment system to support health social security agency

2021· article· en· W3198407226 on OpenAlexvenueno aff
Mochammad Fahlevi, Nouf Alharbi

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Technology, Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentBusinessPayment systemAgency (philosophy)Information and Communications TechnologyThe InternetSocial securityWork (physics)MarketingFinanceComputer scienceEconomicsEngineeringWorld Wide WebSociology

Abstract

fetched live from OpenAlex

The development of the existing information technology era has helped the performance of BPJS more and more. The application of information technology in the work environment includes meeting the efficiency level of time and cost. The use of the internet also makes it easier for BPJS to access their services to the public and the search for new members is made easier. The world of ICT does provide a big role in society. In Indonesia itself, the prospect of ICT in future life has provided a lot of welfare for the people of Indonesia. This research was conducted in Indonesia, the sampling in this study was conducted in Jakarta. The results of research conducted showed that all hypotheses proposed in this study were accepted, especially the biggest influence was found on the attitude variable towards intention in the use of the BPJS e-payment system in Indonesia. Electronic Payment System (EPS) BPJS strives to create a social security system that is transparent and accountable to BPJS Employment participants.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.001
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 head0.125
GPT teacher head0.493
Teacher spread0.368 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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