The brand personalities of brand communities: an analysis of online communication
Bibliographic record
Abstract
Purpose Online brand communities provide a wealth of insights about how consumers perceive and talk about a brand, rather than what the firm communicates about the brand. The purpose of this paper is to understand whether the brand personality of an online brand community, rather than of the brand itself, can be deduced from the online communication within that brand community. Design/methodology/approach The paper is empirical in nature. The authors use community-generated content from eight online brand communities and perform content analysis using the text analysis software Diction. The authors employ the five brand personality dictionaries (competence, excitement, ruggedness, sincerity and sophistication) from the Pitt et al. (2007) dictionary source as the basis for the authors’ analysis. Findings The paper offers two main contributions. First, it identifies two types of communities: those focusing on solving functional problems that consumers might encounter with a firm’s offering and those focusing on broader engagement with the brand. Second, the study serves as a blueprint that marketers can adopt to analyze online brand communities using a computerized approach. Such a blueprint is beneficial not only to analyze a firm’s own online brand community but also that of competitors, thus providing insights into how their brand stacks up against competitor brands. Originality/value This is the first paper examining the nature of online brand communities by means of computerized content analysis. The authors outline a number of areas that marketing scholars could explore further based on the authors analysis. The paper also highlights implications for marketers when establishing, managing, monitoring and analyzing online brand communities.
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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 | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".