Factors affecting credit card use in India
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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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".