Consumer identification in cigarette industry: Brand authenticity, brand identification, brand experience, brand loyalty and brand love
Bibliographic record
Abstract
The cigarette industry faces market competition, and the pressure that suppresses its existence stems from ambiguous policies. The ambiguous policy is because the government still expects cigarette excise income as a significant source, but also that the government is faced with the demands of the anti-smoking community who establish cigarettes as a sunset industry. This condition does not make cigarette business actors give up, given that the cigarette industry still contributes to employment, state income, and market demand is still there. This study aimed to determine the effect of brand authenticity, brand identification, brand experience, brand loyalty, and brand love. The relationship between the five brand theories is examined using the Structural Equation Model. The sample for this study was 200 cigarette consumers, using a non-probability sampling technique. The result shows that: 1) Brand experience significantly and positively impacts brand identification among cigarette industry consumers; 2) Brand identification significantly and positively impacts brand love among cigarette industry consumers; 3) Brand authenticity significantly and positively impacts brand love among cigarette industry consumers; 4) Brand identification significantly and positively impacts brand loyalty among cigarette industry consumers; 5) Brand love significantly and positively impacts brand loyalty among cigarette industry consumers. The product marketing strategy component must take five branding theories. The product's market strengthens with the value of five brand components.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".