Bibliographic record
Abstract
Purpose The purpose of this article is to develop a theoretical explanation – financial numeracy – for consumer proficiency with financial services. With sufficient financial numeracy, consumers benefit fully from financial services and make competent choices in regard to financial management. Design/methodology/approach The article builds theory by combining consumer cognitive capacity and customer knowledge theories with findings from prior studies of consumer difficulties with financial services to introduce a comprehensive model of the antecedents and consequences of financial numeracy with testable propositions for many psychographic and cultural influences and moderators. Findings Financial numeracy demands that consumers possess sufficient financial information processing capacity and ability as well as sufficient prior knowledge of financial concepts. Although partly a function of individual cognitive ability, it can be enhanced through appropriate experience with financial instruments and familiarity through personal financial materials when consumers are motivated to process them. Financial numeracy directly affects financial management outcomes related to borrowing, saving, and taxes. It indirectly affects higher‐order financial consequences, such as a consumer's credit score, interest rates charged on subsequent loans, net worth, likelihood of bankruptcy, and size of inheritance. Originality/value Consumers around the world are increasingly experiencing difficulties with financial services. To advance research in financial services marketing beyond documenting troublesome financial behaviours of consumers, this conceptual model provides insights to help increase consumer proficiency in comprehending and managing financial services based on knowledge about consumer information processing, learning, memory and the cultural and psychographic influences on these internal processes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".