Understanding financial professionals' perceptions of their clients' financial behaviors
Bibliographic record
Abstract
Purpose This article describes financial professionals' perceptions of their clients' financial behaviors and the explanatory factors underlying these behaviors. Design/methodology/approach In this qualitative research, the authors seek to understand financial professionals' experiences in relation to how their clients manage their own finances. The authors conduct and analyze 26 semi-structured interviews with financial professionals from several industries within the financial sector in Canada. Findings The professionals in this study noted that despite their clients' financial knowledge, several other factors can explain these individuals' financial behaviors. They include psychological factors (such as financial bias, the need for instant gratification, and the lack of awareness regarding the long-term effects of certain types of financial behaviors), financial habits (such as lifestyle, financial planning and lack of discipline) and the financial system's flexibility with respect to debt financing and repayment. These perceptions are categorized according to whether they are related to debt financing or repayment, savings or investments. Originality/value By using a qualitative methodology that relies on the perceptions of financial professionals, this study aims to better understand the financial behaviors of individuals and households, and these behaviors' underlying factors. This study's findings could be useful to various stakeholders interested, in one way or another, in financial literacy, such as organizations aiming to strengthen and promote financial literacy, educators, researchers, regulatory bodies of financial institutions and financial advisers.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".