The dimensions of prescription drug brand personality as identified by consumers
Bibliographic record
Abstract
Purpose – The aim of this paper is to describe the development and validation of a two-dimensional scale measuring prescription drug brand personality as identified by consumers. Design/methodology/approach – A total of 483 US respondents rated a subset of 15 well-known prescription medications on 22 different personality traits. A total of 2,245 individual brand evaluations were generated and subsequently analyzed using exploratory factor analysis. Findings – The findings revealed that consumers are in fact able to attribute human personality traits to prescription drugs. A stable and generalizable two-dimensional (competence and innovativeness) scale was established: the Prescription Brand Personality Scale (PBPS). Research limitations/implications – The “stacked” data structure required to aggregate data across subjects discounts the variation between brands and subjects. The brands included in the study are relatively few compared to consumer brands. Practical implications – This research has important implications for the expansion of pharmaceutical branding strategies and demonstrates the potential of using brand personality as an effective positioning and differentiation tool. Originality/value – This is the first study to investigate the existence of prescription drug brand personalities as perceived by consumers as well as the development of the PBPS, specifically for prescription drug brands. The findings have important implications for the development of innovative marketing strategies, and this study lays the groundwork for further investigation into the antecedents and consequences of prescription drug brand personality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".