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Record W3120980005 · doi:10.5267/j.ac.2020.12.007

An integrated model of financial well-being: The role of financial behavior

2021· article· en· W3120980005 on OpenAlexvenueno aff
Rr. Iramani, Lutfi Lutfi

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyFinanceIndirect financeFinancial analysisFinancial modelingAccounting managementStructural equation modelingAffect (linguistics)BusinessFinancial planMarital statusStrategic financial managementPsychologyAccountingMarketingComputer scienceSociology

Abstract

fetched live from OpenAlex

One of the main goals of every individual or household is to achieve financial well-being. Previous research has shown that various factors influence financial well-being. This research aims to develop an integrated family financial welfare model by examining various factors that affect it. This study uses data of 1,158 households taken using an online survey. The data is analyzed using a structural equation model. The results show that financial experience, financial knowledge, financial status, and marital status directly affect financial well-being. Financial behavior significantly mediates the influence of financial behavior, financial knowledge, and locus of control on financial well-being. Furthermore, marital status strengthens the effect of financial knowledge on financial well-being, but it does not strengthen the effect of financial experience on financial well-being. This study suggests that the Government and financial authorities need to improve further the effectiveness of financial literacy and financial inclusion programs and campaign for a more frugal life among households to avoid financial difficulties.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations107
Published2021
Admission routes1
Has abstractyes

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