The Impact of Website Design and Customer Support on Customer Experience and Its Relation to Fintech Adoption Intention in Saudi Arabia
Bibliographic record
Abstract
Very little research has been conducted on customer experience (CE) in fintech to date, especially in the Kingdom of Saudi Arabia. The purpose of this paper is to study the impact of website design (WD) and customer support (CS) on CE and its relation to fintech adoption intention (AI) in Saudi Arabia. Data was gathered from 296 individuals by means of a survey. After checking the validity and reliability of the results, structural equation modeling was used to test the hypotheses. The results showed that WD and CS have a significant positive impact on CE. Also, WD and CE positively influence AI in fintech, while CS does not have a direct relation. Furthermore, CE mediates both WD and CS with AI. This paper provides further conceptual understanding of antecedents and consequences of CE in fintech. Practitioners in the fintech sector could benefit from results in achieving long-term goals. There are some limitations in the study that suggest the need for further studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".