Cultural Variations in Reasons for Advice Seeking
Bibliographic record
Abstract
Abstract Five studies examined cultural differences in reasons for advice‐seeking behaviors. Content analyses in Study 1A and self‐ratings in Study 1B consistently revealed that Euro‐Canadians were more likely than East Asians (mainly Chinese) to seek advice for informational reasons, whereas East Asians were more likely than Euro‐Canadians to seek advice for relational reasons. Study 2A showed that Chinese displayed a higher level of relationship concern than Euro‐Canadians in deciding from whom to seek advice in a decision dilemma. Study 2B found that, although Chinese and Euro‐Canadians did not differ from each other on willingness to pay for informational advice, Chinese were willing to pay more for building a relationship with the advisor through advice seeking than Euro‐Canadians were. Study 3 explored how the advice giver might perceive an advice seeker in terms of their competence and the closeness of their relationship after advice was sought for various reasons. We found that relationally oriented advice seeking increased the perceived competence of the advice seeker among Chinese more than among Euro‐Canadians. Information‐oriented advice seeking increased the perceived closeness between the advice seeker and advice giver among Chinese more than among Euro‐Canadians. Implications for other aspects of advice exchange are discussed. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".