Islamic financial technology acceptance: An empirical study in Jordan
Bibliographic record
Abstract
The present research endeavors to comprehend the determinants that impact the inclination to utilize financial technology within the context of Islamic banking clientele. The study undertakes an examination of various determinants that may exert an influence on the consumer's intention. These determinants encompass financial risk, legal risk, security risk, operational risk, consumer innovativeness, perceived ease of use, and perceived usefulness. The technology acceptance model is employed as the theoretical framework for the research. The requisite data for hypothesis testing is collected through the administration of an online survey to consumers of Islamic banks who possess a high degree of adaptability and proficiency in utilizing financial technology. The study employs the methodology of structural equation modelling with partial least squares to assess the proposed relationships among a sample of 399 participants. The results indicate that the acceptance of Islamic Financial technology services is contingent upon the perceived ease of use, perceived usefulness, and consumer innovativeness. In contrast, it is observed that various other factors, namely financial risk, legal risk, security risk, and operational risk, do not hold significant sway in shaping the level of acceptance of Islamic Financial technology among users of Islamic banking services. The concept of Technology Acceptance Model (TAM) is expanded within the realm of Islamic financial technology, and it is utilized to examine the impact of a novel factor, specifically consumer innovativeness. The untested nature of consumer innovativeness makes this paper a valuable resource for policymakers, academics, and researchers in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".