The Role of Self-Construal in Group-Buying Propensities of Chinese and Canadian Generation Z
Bibliographic record
Abstract
Today, the use of group-buying platforms is expanding and diversifying, affecting both consumers and businesses. This paper seeks to contribute to the discussion of these platforms' global market segmentation by providing a cross-cultural understanding of the correlations between self-construal level, group-purchasing behaviour, and underlying group-purchase incentives predicted by self-construal theories among Generation Z. Data were collected from current Chinese and Canadian undergraduates at McGill University. The results reveal different relationships between self-construal, group-buying incentives, and group-buying propensity in both participant groups. For group purchases with friends (Study 1), the level of interdependent self-construal is associated with an increased likelihood for Chinese participants to share their purchase list. Nonetheless, in both participant groups, self-construal level is not associated with a propensity to accept purchase invitations from friends, while conversely, the intention to strengthen friendship bonds is a reason for accepting such invitations. For group purchases with strangers (Study 2), the level of interdependent self-construal positively correlates with both participant groups’ pursuit of group savings. Lower levels of self-construal also increase the level of pursuit of popular items among the Chinese participants. The findings shed light on the mentalities behind group-buying propensities in Generation Z, and they reinforce the need for culturally tailored managerial approaches for group-buying platforms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".