Factors and Determinants of Financial Behaviors That Undermine Financial Well‐Being: A Qualitative Study
Bibliographic record
Abstract
ABSTRACT This paper aims to identify the factors, their determinants, and indicators contributing to financial behaviors that may be detrimental to people's financial well‐being. A qualitative methodology was employed, involving financial professionals and members of the general population. The theory of planned behavior was inductively used as an analytical framework. The results suggest that detrimental financial behaviors are shaped by (1) attitudes such as financial apprehension, rigid financial mindsets, and lack of awareness; (2) social influences including relational pressure, sociocultural norms, geographic context, social media, and marketing; (3) perceived behavioral control factors such as limited knowledge, environmental conditions, financial ecosystem, and insufficient education; and (4) ingrained financial habits, including avoidance, cognitive biases, overconsumption, and lifestyle orientation. The originality of this research lies in the new insights it offers into the cognitive and psychological processes shaping financial behaviors. The findings interest stakeholders such as government bodies, financial education developers, researchers, and regulators.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".