The role of impulsivity and financial satisfaction in a moderated mediation model of consumer financial resilience and life satisfaction
Bibliographic record
Abstract
Purpose This paper explores the direct and indirect associations between financial resilience and life satisfaction, using the moderation of non-impulsive behavior and mediation of financial satisfaction. Design/methodology/approach The authors analyze the Australian household dataset, named the Household, Income and Labour Dynamics in Australia (HILDA) Survey, to meet the objectives of this paper. Furthermore, the authors use the PROCESS Models 4 and 7 to test the mediation and the combined moderated mediation relationships, respectively. Findings The authors find the complete mediation of the relationship between financial resilience and life satisfaction by financial satisfaction. Also, this study finds that both financial resilience and non-impulsive behavior positively contribute to financial satisfaction, which is positively associated with life satisfaction. Practical implications This research supports the need for consumers to build emergency funds as financial resilience is related to consumer well-being. This research also recommends that impulsive behavior should be addressed by the personal finance curriculum and financial advisors. Originality/value This research contributes by showing that financial satisfaction is an important predictor of consumers’ well-being. The ability to access financial resources, which increases for non-impulsive consumers, is associated with increased life satisfaction but only via financial satisfaction.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".