Cultural Finance : Does culture influence investment preferences in Austria and Canada?
Bibliographic record
Abstract
This study delves into the impact of culture on investment preferences in Austria and Canada. By exploring how culture influences important factors in investment decision-making, such as risk, time, and trust preferences, this research sheds light on the significance of the cultural context in finance. Quantitative research methods were employed, utilizing a questionnaire that was administered to a sample of 98 Austrians and 68 Canadians. The analysis of the data involved the use of t-tests to test the stated hypotheses, as well as correlation analysis to uncover additional insights. The findings of the study reveal distinct differences between Canadians and Austrians in terms of their investment behavior. Canadians were found to be more inclined towards risk-taking, exhibit greater patience, and display higher levels of trust compared to their Austrian counterparts. Moreover, the study highlighted that Canadians tend to invest in riskier assets and prefer longer- term investments. By focusing on Austria and Canada, this study contributes to the existing literature by examining the cultural influences of countries that have received less attention in this field. It expands our understanding of how culture shapes investment preferences in diverse cultural contexts. The research findings contribute to our understanding of the differences between these two cultural contexts and offer relevant information for practitioners in the investment industry, aiding them in tailoring their services to better meet the needs and preferences of their clients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".