Perceptual exploration of credit cards' adoption: Customer perspective
Bibliographic record
Abstract
Despite the increased dissemination of online tools to execute financial transactions, the level of credit card usage adoption among consumers still encounters many challenges. The dissemination and adoption of credit cards to execute online purchases is influenced by certain factors that impact customers' consumption behavior. Exploring and perceiving these factors and challenges is imperative for enhancing the operations of online businesses. This study aims to examine specific factors that affect the adoption of credit cards among customers within the Jordanian online market. The factors this study explores are: Expenditure Level, Welfare of the Individual, Psychological Behavior, Credit Card Knowledge, Regulations and Laws and, finally, Theft and Fraud. This research was conducted through employing the quantitative approach; utilizing a questionnaire on a total of (335) credit card users in Jordan. The research subjects were credit card users (customers) from three Jordanian banks: Arab Banking Corporation, Housing Bank for Trade and Finance, and the Bank of Jordan. The study findings indicated that ‘Individual Welfare’ and ‘Psychological Behavior’ are the strongest influential factors on individuals' adoption of credit cards, followed by ‘Theft and Fraud’, ‘Laws and Legislation’, ‘Credit Card Knowledge’ and ‘Expenditure Level’ respectively in influence. The study recommendation encourages banks to focus more on increasing their clients' awareness regarding credit cards usage in order to enhance their perception of how to behave in case of fraud and theft and increasing their clients' awareness of the financial burdens, pitfalls, and tricks while using a credit card. Moreover, it suggests that banks should increase their marketing efforts for credit cards which can change the behavior and acceptance of individuals towards credit cards adoption.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".