The influence of money attitudes on young Chinese consumers' compulsive buying
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how young Chinese consumers' money attitudes influence their compulsive buying behavior. Design/methodology/approach In total, 303 undergraduate students from Tianjin and Ningbo (two major cities in coastal China) answered a self‐administered questionnaire. Findings Money attitudes were found to significantly affect young Chinese consumers' compulsive buying behaviour. Specifically, the Retention‐Time dimension significantly affected both male and female consumers' compulsive buying. However, the Power‐Prestige dimension only affected male consumers' compulsive buying. Finally, the Quality dimension had a greater impact on male than on female consumers' compulsive buying. Research limitations/implications The data were collected in two major cities in the coastal region of China. Given the differences between coastal and inland China, caution must be taken when generalizing the research results to young consumers from inland China. Practical implications The discussion of the relationships between young Chinese consumers' money attitudes and their compulsive buying will help marketers and policy makers to better understand these consumers' spending behaviour. Thus, marketers can identify new market opportunities and form marketing strategies to target young consumers in China. On the other hand, policy makers can also form more effective education strategies to help young consumers to spend wisely. Originality/value Different from previous research in money attitudes and compulsive behaviour, the research provides an in‐depth overview of how male and female young Chinese consumers perceive money and how their beliefs about money affect their spending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".