The Mediating Effect of Representativeness Heuristic on Neurofinance and SME's Financial Decision Making
Bibliographic record
Abstract
Financial decision-making is a crucial part of business survival, especially among SMEs. About 95% of the business are facing failures within five-year time. The financial decision making failure happened due to psychology and behavioural. This research aims to determine the mediating effect of representativeness heuristic on emotions and financial decision making. A pre-test and post-test experiment analyzes emotions, financial decision-making, and representativeness heuristic behaviour. In pre-testing, emotions and financial decision-making questionnaires are measured using questionnaires distributed to forty-two SMEs. Then, the video clips with 12 to 16 minutes duration are used in manipulating the emotions from neutral emotion to positive and negative emotions. Lastly, in post-testing, the data are gathered by repeating answered emotion and financial decision-making questionnaires, followed by the representativeness heuristic questionnaire. The data were analysed using General Linear Regression. The results showed that representativeness heuristic is partially effect on negative emotion towards financial decision making. From the analysis, neuro-behavioural of financial decision-making model has been proposed. The proposed models are incorporating with the brain components and working memory. It shows that System 1 and System 2 of the dual-process theory are activated for negative and positive emotions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".