Factors affecting credit card use in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the moderating influence of Multi‐item List of Value (MILOV) on credit card attributes, age, and gender in credit use among Indian customers. The research examines the impact of “lifestyle” variables (convenience, use patterns, and status) on credit card use. Design/methodology/approach Data were collected through mall intercept technique in six metropolitan cities of India. A self‐administered questionnaire was distributed to customers visiting the malls. Findings Use and convenience emerged as the major determinants of credit card use among Indian customers. Use, convenience, and status attributes were moderated by “sense of belonging” and “sense of fulfilment” dimensions of MILOV. Young customers were likely to use credit cards. Research limitations/implications The study does not examine the influence of customer income, occupation, and education on credit card use, as many customers were not willing to disclose the information. These demographic factors can influence customers' perception towards credit card ownership and use. Practical implications The findings can be of immense use to international and Indian banks in marketing of credit cards. The convenience attribute can be emphasized to instill confidence among consumers and motivate them to use credit cards. Originality/value There is no previous research on Indian credit cards which examines the influence of “lifestyle” and values on its use among Indian customers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it