Communicating brand personality: are the web sites doing the talking for food SMEs?
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyse web site brand communication by small to medium‐sized enterprises (SMEs) in the restaurant franchise industry, using Aaker's brand personality dimensions. It shows how an SME can test its intended positioning against competitors. Design/methodology/approach A multistage methodology using a combination of content analysis and correspondence analysis was used. The intention was to illustrate a technique that can be used by SMEs at low cost and with ease. Findings Food SMEs are able to communicate brand personality by way of their web sites. The brands and the personality types are presented which clearly reveals the positioning of the competitors. Practical implications This paper illustrates a powerful, but simple and relatively inexpensive way for SMEs to study communicated brand personality. Originality/value The major contribution of this study is to alert SME scholars and retailers to the potential of computerized content analysis as a means of studying web site content, and the subsequent use of correspondence analysis to understand how to position against competitors.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".