Bibliographic record
Abstract
Purpose This study aims to investigate investing and borrowing behavior in retail banking between ethnic groups, specifically the Caucasians vis‐à‐vis the Chinese. Design/methodology/approach A total sample of 645 Caucasians and Chinese in Australia, Canada and China were tested for their level of business assigned to their main banks, defined as share of wallet (SOW) in this study. The study applied multivariate analyses. Findings No significant differences were found between the ethnic Chinese in Australia and Canada in comparison to their counterparts in mainland China, or compared with the Caucasians in Australia and Canada. This finding of convergence suggests that ethnic Chinese have adapted to the local banking behavior. The ethnic Chinese in Australia and Canada assigned 81‐88 percent of their assets to their main banks, in comparison to only 72 percent for their counterparts in China and 73 percent for the Caucasians. As such, the ethnic Chinese in Australia and Canada have developed their own unique behavior, resulting in crossvergence: an over‐adaptation to local behavior in managing their assets, and a mid‐way approach between the Chinese in China and the local Caucasians when it comes to borrowing money. Practical implications For bank marketing managers, this form of crossvergence constitutes a challenge as it suggests that gaining the trust of Chinese customers is complex since the SOW is lowest in the booming emerging market (i.e. China) whereas ethnic Chinese consumers in Western markets have formed their own unique pattern of allocating business to their banks. “Ethnic banking” is suggested to offer tailored services to ethnic groups in order to satisfy their specific money management. Originality/value This study establishes that Chinese consumers in Western markets are a distinct consumer group. Products and services need to be specially customized to suit their wants and needs.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".