Financial literacy and financial well-being of Australian consumers: a moderated mediation model of impulsivity and financial capability
Bibliographic record
Abstract
Purpose This study aims to test a moderated mediation model for a twofold purpose. First, to examine the mediating role of financial capability (FC) in the association between financial literacy (FL) and financial well-being (FW). Second, to analyze if non-impulsive future-oriented behavior (NIB) moderates the associations of FL with FC and FL with FW. Design/methodology/approach The authors use the PROCESS macros in IBM SPSS Statistics to test the moderated mediation model and analyze the 2016 wave of the Household, Income and Labor Dynamics in Australia Survey. Findings The empirical analysis shows that FC partially mediates the association between FL and FW. Furthermore, the moderated mediation analysis shows that NIB strengthens the associations of FL with FC and FL with FW. Specifically, the positive associations of FL with FC and FL with FW significantly increase for those consumers who score high on NIB. Practical implications The findings have implications for the financial services industry. Professional financial planners can positively improve the ability of consumers to deal with their financial matters by highlighting the importance of FL and NIB. Social implications The study findings suggest educating consumers to discourage impulsive behavior and encourage them to create financial plans as it will enhance their ability to conduct financial tasks efficiently, improving their FW. Originality/value To the authors’ knowledge, this is the first study to assess a moderated mediation model, which examines the role of FC as a mediator variable and NIB as a moderator variable in the association between FL and FW.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".