A Study of the Financial Behavior Based on the Theory of Planned Behavior
Bibliographic record
Abstract
Personal finance and investments are closely related to people’s lives. Most investors focus on return rates than on risks. However, when the market changes considerably or unexpectedly, investors may incur losses. Employees in the electronics industry are a middle- to high-income group. However, their work may prevent them from acquiring financial knowledge and factors affecting investments. This group should also understand methods of reducing risks in investments and avoiding losses. This study examines employees in the electronics industry in Taiwan and uses the theory of planed behavior to investigate the effects of demographic variables and intention to manage personal finances and invest on investment behavior. A total of 600 questionnaires were distributed, and 469 were returned. Among the 469 questionnaires, 41 were incomplete and invalid and thus disregarded, posting a response rate and valid response rate of 78.16% and 71.33%, respectively. SPSS 20 and structural equation modeling were used for analysis. The results demonstrate that financial attitude has a significant and positive effect on financial knowledge and subjective norms, that subjective norms have a significant and positive effect on perceived financial control, and that financial knowledge has a significant and positive effect on financial behavioral intention. However, subjective norms and perceived financial control do not significantly affect financial knowledge. The results suggest that individuals should reduce risks in their personal finances and investments. Potential directions for further research are also provided.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".