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Record W3197576151 · doi:10.5267/j.ijdns.2021.8.007

Analyzing cashless behavior among generation Z in Indonesia

2021· article· en· W3197576151 on OpenAlexvenueno aff
Raden Aswin Rahadi, Nindya Resti Ramadhani Putri, Subiakto Soekarno, Sylviana Maya Damayanti, Isrochmani Murtaqi, Jumadil Saputra

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentExpectancy theoryBusinessLife expectancyUnified theory of acceptance and use of technologySocial securityMarketingActuarial scienceEconomicsFinanceEnvironmental healthManagementMedicine

Abstract

fetched live from OpenAlex

Secure, convenient, and affordable payment instruments are one factor that drives up the development of the national economy. Having a good and stable national economic condition is the intention of every country. The usage of electronic payment instruments is proven to boost economic growth and advance financial inclusion. However, the usage of e-payment among generation Z in Indonesia is still relatively low. Of these, this study is written to analyze the factors influencing electronic payment used to be taken as a concern on evaluating the current level of the cashless society. The model to assess the influencing factors is adopted from UTAUT variables: performance expectancy, effort expectancy, and social influence, combined with two external variables: culture and perceived security. The questionnaire is distributed to 458 respondents, covering generation Z in Bandung City. A quantitative approach was used to assess the questionnaire result, examining the relationship between each factor and electronic payment usage. The results indicate three factors that significantly influence electronic payment usage among generation Z in Bandung City: performance expectancy, social influence, and culture.

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 score0.048
Threshold uncertainty score0.843

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 communication0.0010.007
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 head0.045
GPT teacher head0.299
Teacher spread0.254 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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