The Impact of Distribution Intensity on Brand Preference and Brand Loyalty
Bibliographic record
Abstract
Several studies attempted to conceptualize and measure brand equity. Brand equity constructs identified include awareness, associations, perceived quality, and loyalty, among others. Further, brand performance has been operationalized in terms of market share, ability to charge price premium, and distribution coverage. While most studies focused on consumer-based constructs, few researchers tested the effect of distribution intensity on brand performance. This study advances a model that links distribution intensity with brand preference and loyalty, and empirically tests it on the fuel industry in Egypt. First, in-depth interviews with industry experts were conducted to validate research hypotheses. Then, online surveys were distributed to test model relationships on four leading brands. Results revealed that affect, satisfaction, perceived quality, as well as distribution intensity significantly affected brand preference; which in turn was the key driver to brand loyalty. It is recommended that firms consider the role of distribution while developing marketing strategies and brand-building activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".