Bibliographic record
Abstract
The objective of this paper is to empirically explain how the trust in banks is influenced by the nominal, performative, altruistic and axiological factors in different age-groups of the consumers.The aim of this paper is to answer what factors influence the trust in banks.The structural equation model (SEM) was used in the empirical research to model how trust depends on the type of expectations that a customer has from a bank as well as the essence of the overall trust.The empirical database was comprised by the results of the surveys conducted by the author in the fourth quarter of 2016 on a representative nationwide sample of N=3000 people aged 15 and over.The SEM estimating model yielded positive verification of the model hypothesis, according to which, the trust in banks is built by the normative, performative, altruistic and axiological determinants.The most important thing in building the trust in banks is the banks' proper response to the consumers' normative expectations that they have from the banks.Secondly, the axiological determinants are responsible for the trust in banks.
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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 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".