Is your company competent, interpersonal or community focused? The effect of values and brand portfolio on company reputation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is twofold: first, to determine if pharmaceutical companies can be grouped based on their espoused values, and second, to examine the relationship between these values and company reputation. Design/methodology/approach A descriptive study design is used with two separate analyses: cluster analysis for grouping the companies; and descriptive data analysis for determining cluster differences. Findings The findings suggest that there are three value clusters: competent, community and interpersonal, with the community group showing the highest relative reputation, and the interpersonal cluster as the lowest. Brand portfolio composition appears to positively contribute to reputation. The effect of portfolio specialization is based on a company’s closeness to its therapeutic community, which may be influenced by the outward characteristics of its values. Research limitations/implications Future research should examine the longitudinal effects of values on reputation combined with case studies. Practical implications Regardless of cluster classification, all firms should develop strong ties with their therapeutic communities using both personal and digital/omnichannel strategies. Social implications A company’s values are becoming an important consideration for all customers and stakeholders. Originality/value To the best of the authors’ knowledge, this study is the first to systematically examine the activities of leading pharmaceutical firms to link a specific value cluster to company reputation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| 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 it