Impact of Immigration on Native and Ethnic Consumer Identity via Body Image
Bibliographic record
Abstract
This research focuses on consumer identity of two under-researched but growing immigrant communities in Australia via the lens of the body image construct. Consistent with an emerging stream of research, body image is viewed as a part of identity. Given the variety of goods and services that have an impact on consumers’ perceptions of their body, and because consumers use products to create and convey desired identities, body image is also viewed as a part of consumer identity. Considering literature on identity, body image, and acculturation, exploratory research was undertaken to determine the impact of immigration on the identities of both immigrants and natives. Specifically, focus groups were conducted on two generations of Filipino- and Indian-Australian women as well as Anglo-Australian women. It was found that second generation immigrants have dual consumer identities where they balance the values, attitudes and lifestyles of both their home (i.e., native or heritage) and host cultures whereas first generation immigrants tend to retain their native consumer identity even if they appear to adopt values, attitudes, and lifestyles of the host culture. The impact of immigrants on consumer identities of native residents who are typically in the majority (i.e., the Anglo group) was not evident. Theoretical and practical implications including recommendations for marketing practitioners are then discussed followed by suggestions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".