Millennial Generation’s Islamic Banking Behavioral Intention: The Moderating Role of Profit-Loss Sharing, Perceived Financial Risk, Knowledge of Riba, and Marketing Relationship
Bibliographic record
Abstract
Despite tons of studies on Islamic banking (IB) behavior, there is a lack of understanding of the Millennial generation’s attitude to and subjective norms surrounding Islamic banking, as well as of their behavioral intention toward Islamic banking. Therefore, the present study investigates the influence of the Millennial generation’s attitude and subjective norms on their behavioral intention toward Islamic banking products and services. This study also focuses on the moderating roles of profit-loss sharing, perceived financial risk, knowledge of riba, and relationship marketing on the nexus of antecedent and behavioral intent of Islamic banking. This study has developed a conceptual framework, employed a questionnaire to collect data for understudying relationships, and constructed a predictive model. Within the proposed conceptual framework, structural equation modeling is employed to investigate the extent and direction of the link. We discovered that Millennial generation consumers’ attitudes and subjective norms influence and predict their behavioral intention towards Islamic banking. With the exception of perceived financial risk, all moderators have direct effects on behavior intention toward Islamic banking and could be antecedents of behavior intention toward Islamic banking. Profit-and-loss sharing and knowledge of riba moderate the nexus of attitude and behavioral intention and the nexus of subject norms and behavioral intention. Our findings thus extend the literature on Islamic banking and consumer behavior context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".