Nonlinear Antecedents of Consumer Satisfaction on E-Banking Portals
Bibliographic record
Abstract
This study verifies the nonlinearity of main inductors of customer satisfaction with the quality of online banking services. To that end, we performed an exploratory and quantitative research, through a survey applied to a sample of 256 respondents, e-banking service users. Using an exploratory factorial analysis, we identified eight input dimensions, composed of 33 relevant attributes, listed as inducers of the quality of e-banking services. Then, we used a non-linear regression technique (Penalty Reward Contrast Analysis) to classify dimensions related to the quality of the e-banking services as “basic”, “one-dimensional”, “attractive” or “neutral”, and compared the outcomes of this method to a linear regression analysis. The results point out that, controlled by gender and education, the dimensions “support to transactions” and “safety” are one-dimensional attributes; the dimensions of “convenience,” “decision support,” and “problem solving” are basic attributes, and “design” and “benefits” can be considered as attractive or exciting attributes. In addition, the results show that the nonlinear analysis explains 12.5% better the variance (Adj. R2) of general customer evaluation of the service, than traditional linear analysis. The contribution of this study consists in clarifying the service quality factors affecting customer use of Internet banking services, valuable to improve quality 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".