Understanding Consumer Behavior in Digital Banking: The DABU Model as an Extension of TAM and UTAUT
Bibliographic record
Abstract
The aim of this research was to examine the impact of digitalisation on the use of banking products among consumers in the Republic of Croatia, with a particular focus on analysing habits, perceptions, and barriers related to digital banking. The study was conducted on a convenience sample of 820 respondents—citizens of the Republic of Croatia—representing diverse age groups, education levels, and degrees of digital literacy. To gain deeper insights into consumer behaviour, a new conceptual model—DABU (Digital Acceptance of Banking Use)—was developed. This model integrates elements of existing theoretical frameworks (TAM, UTAUT, CBM) and introduces additional variables relevant to the context of digital banking, such as digital literacy, perceived security, and perceived barriers. Data were collected using a structured survey questionnaire and analysed through descriptive statistics, factor analysis, and structural equation modeling (SEM). The findings indicate that digital literacy and perceived security are key predictors of the intention to use digital banking services, whereas perceived barriers have a significant negative effect. Moreover, differences in digital banking usage patterns were observed across age groups, education levels, and prior experience with digital technologies. The results contribute to a better understanding of the factors influencing consumer digital behaviour and provide practical guidelines for developing targeted strategies within the financial sector aimed at increasing the adoption and accessibility of digital banking services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".