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Record W4405258716 · doi:10.5267/j.dsl.2024.10.008

The role of mental accounting and financial attitudes in shaping financial behavior among entrepreneurial students using fintech

2024· article· en· W4405258716 on OpenAlexvenueno aff
Wirawan Endro Dwi Radianto, Heribertus Andre Purwanugraha, Heru Kristanto, Tommy Christian Efrata, Ika Raharja Salim

Bibliographic record

VenueDecision Science Letters · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyFinanceAccounting managementFinancial accountingMental accountingFinancial ratioBusinessAccounting information systemAccounting

Abstract

fetched live from OpenAlex

This study examines the influence of mental accounting, financial attitudes, financial knowledge, and financial self-efficacy on the financial behavior of diligent students in using fintech. This study differs from previous research because it uses a sample of students who already have a business and often use fintech. Data collection was carried out through the distribution of questionnaires to respondents. This study found that financial attitudes and self-efficacy had a significant effect on financial behavior, while financial knowledge had no significant effect on financial self-efficacy and mental accounting. Mental accounting significantly affects financial attitudes, financial behavior, and financial self-efficacy. These findings prove the importance of mental accounting in increasing the confidence and effectiveness of students who often use fintech and have a business in making financial decisions. This research contributes to developing the theory of planned behavior in the context of financial behavior. It reveals that financial literacy does not necessarily increase financial self-efficacy and mental accounting, especially among students who often use fintech and are just starting a business.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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