The Differences of Asian and Western Consumers’ Attitudes towards Brand Extensions by Information Types: Attribute-Related vs. Non-Attribute-Related Information
Bibliographic record
Abstract
This study aims to empirically examine how Asian and Western consumers with different cultural backgrounds (holistic vs. analytic thinking) in different brand extension situations (high vs. low brand-extension fit) perceive two different types of brand extension information (attribute-related vs. non-attribute-related information).The previous brand extension studies have demonstrated that Asian consumers are considerably better in recognizing fit between parent brands and their extensions than Western consumers.However, only few studies have been conducted so far to investigate how firms can effectively communicate with consumers from different cultures when extending their existing brands.For that, an inter-subjects experiment consisting of 2 (high vs. low similarity with parent brands) *2 (attribute-related vs. non-attribute-related information) *2 (Asian vs.Western consumers) groups was conducted with the samples from South Korea, US, Canada and France (N = 393).As a result, Westerners tended to show more favor to attribute-related information than Asians when brand-extension fit was high.When brand-extension fit was low, however, Asians tended to show more favor to attribute-related information than Westerners.In addition, Asians overall showed more favor to low-similarity extensions compared to Westerners when non-attitude-related information was suggested.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".