Investigating the Effect of Selected Marketing Efforts in Brand Equity Creation and Its Cross-Cultural Invariance in Emerging Markets
Bibliographic record
Abstract
In the field of brand management, numerous studies have been conducted on brand equity conceptualization, measurement and validation. Also, previous researchers have shown that consumer-based brand equity via its dimensions can be created and maintained through a company’s marketing mix activities. Brand equity according to Keller, is the differential effect of brand knowledge on consumer response to the marketing activities performed on the brand. Due to cultural differences, consumers’ reaction will differ and thus these marketing efforts will have varying results in different markets.Drawn from both Aaker’s & Keller’s conceptualizations of brand equity, the current study develops a brand equity creation process model similar to Yoo et al.’s and examines its cross-cultural invariance through a structural invariance test using data from two important growing markets—Ghana and China. Results prove that some marketing efforts and dimensions of brand equity have invariant effects on brand equity across the Ghana and Chinese samples. Specifically, the effect of price on perceived quality was not equivalent in both markets. Relationship among brand equity dimensions were also not equivalent, however these dimensions all show an equivalent, positive effect on brand equity. Managerial implications for international brands and limitations for future research are discussed.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".