Motivations for personal financial management: A Self-Determination Theory perspective
Bibliographic record
Abstract
Financial knowledge and sound financial decision making are now broadly recognized to be important determinants of both personal and societal prosperity, but research has yet to examine how distinct qualities of motivation may be associated with the way people manage their money. In two studies we applied the framework of Self-Determination Theory (SDT) to examine people's autonomous (volitional) and controlled (pressured) motivation for understanding and managing their finances, as well as their amotivation (lack of motivation) for doing so, and the differential associations these motives have with financial knowledge and financial well-being. American participants (Study 1, N = 516; Study 2, N = 534) completed detailed demographic surveys and questionnaires assessing the financial variables of interest. As hypothesized, SDT's motivational constructs were associated with financial outcomes over and above participants' age, gender, income, household wealth, and educational attainment. Autonomous motivation was positively associated with a host of positive financial behaviors and characteristics (e.g., saving/investing and financial self-efficacy, well-being, and self-awareness). Controlled motivation was negatively associated with financial well-being. Amotivation was positively associated with overspending and negatively associated with financial self-efficacy and well-being. These findings support the relevance of SDT's framework in this domain and suggest that interventions aimed at promoting financial knowledge and wellness may benefit by adopting evidence-supported strategies for optimizing more autonomous motivations and addressing amotivations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".