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Record W4307964394 · doi:10.3390/jrfm15110488

Financial Literacy of Adults in Germany FILSA Study Results

2022· article· en· W4307964394 on OpenAlexvenueno aff
Michael Schuhen, Susanne Kollmann, Minou Seitz, Gunnar Mau, Manuel Froitzheim

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyCompetence (human resources)Construct (python library)Structural equation modelingThe InternetPsychologyOrder (exchange)FinanceBusinessMedical educationComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The steady growing of online financial services due to the vanishing of on-site banking offers is changing the socio-economic framework individuals make financial decisions in. Financial literacy as an essential part of basic education is therefore also subject to changes. In order to investigate the individual competence of the respondents with regard to financially determined life situations, a digital questionnaire survey with integrated simulation sequences was conducted. For this purpose, a testing instrument (FILSA—Financial Literacy Study of Adults) has been developed to measure the financial literacy of adults. The validity of the construct including its five content areas was tested and the relationships between the manifest exogenous variables and financial literacy were mapped in a structural equation model. It introduces the participants (N = 212) to various financial problems and offers specific aids for founded decision-making. The study’s evaluation system takes into account the participants’ individual behavior in three case studies as well as the impact of attitude and socio-demographic factors on their decisions and behaviors, such as gender, age, and their degree of internet affinity. FILSA examines how adults make decisions with regard to finances and what benefit or impact online tools and financial advisors have on the decision-making process. Furthermore, it is possible to develop concepts for self-learning that are comprised in online tools for decision-making.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.006
GPT teacher head0.215
Teacher spread0.210 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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