Personal Finance Management Skills and Financial Sustainability Literacy Knowledge of Generation Y: An Empirical Analysis in Lithuania
Bibliographic record
Abstract
Purpose We research Generation Y personal finance management skills by integrating three financial literacy measurement perspectives: financial knowledge, behaviour, and attitude towards sustainability. Design/methodology/approach A financial literacy measurement approach aligned with the OECD methodologies and comparative approach to evaluate the financial literacy competence of the Lithuanian youth (N=426) in the global context were applied. Analysis of variance using Levene’s test for equality of variances and t-test for equality of means were employed to check for the differences in Generation Y financial literacy patterns. The correlation between all three financial literacy perspectives was evaluated. Findings The results unfold Lithuanian millennials’ intermediate level financial literacy competence: moderate financial knowledge, positive financial behaviour, and more positive financial attitude towards sustainability, where the latter exceeds the global sustainability concern. Furthermore, our tests indicate the differences in Generation Y financial knowledge in terms of gender and education and the differences in their attitude on financial sustainability in terms of gender, education, income source, and monthly income. Moreover, our research evidences the statistically significant proportional relationship between Generation Y financial behaviour and their attitude towards the sustainability principles application in the financial services. Originality/value In terms of financial literacy, global research extensively focuses on selected countries; Lithuania, the Baltic States, or Eastern and Central European countries are seldom considered. Previous research identifies the existing differences between age groups when evaluating their financial literacy and level of personal finance planning skills. In contrast, our research contributes to increasing the body of knowledge of financial sustainability literacy, and to a better understanding of financial literacy by shedding light on the patterns of Generation Y in Lithuania and provides roots for developing insights for both finance literacy policy makers and financial service providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".