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Record W3165915205 · doi:10.5267/j.ac.2021.5.011

Can financial literacy become an effective mediator for investment intention?

2021· article· en· W3165915205 on OpenAlexvenueno aff
Kelvin Tanuwijaya, Ignatius Roni Setyawan

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyTheory of planned behaviorSocializationInvestment (military)FinanceSocial learning theoryFinancial marketControl (management)BusinessEconomicsPsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The lifestyle of Indonesian people who are very consumptive makes it difficult for people to invest. This can be shown in the number of capital market investors in Indonesia which is only 0.61% of the total population. The low level of financial literacy in Indonesia is one factor. Many people do not understand finance so they cannot manage finances properly. In this study, we look for 130 respondents who are college students to find out how financial socialization and financial experience influence on investment intention through financial literacy. The theory used in this research is theory of planned behavior and social learning theory. In this study, financial literacy can only mediate the financial experience of investment intention. The results of this study are in accordance with the theory of planned behavior in which one of the elements is perceived behavioral control with self-control factors originating from within, namely experience so that the financial experience is expected to generate interest in investing.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.010
GPT teacher head0.255
Teacher spread0.244 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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