Effect of pharmaceutical companies’ corporate reputation on drug prescribing intents in Romania
Bibliographic record
Abstract
This research examines the effect of pharmaceutical companies’ (PCs’) corporate reputation on drug prescribing intents. The aim is to determine the extent to which the PCs’ corporate reputation influences general practitioners’ (GPs’) drug prescribing intents. This research is based on quantitative analysis using structural equation modelling (SEM) on data collected from a sample of 177 Romanian GPs. The PCs’ corporate reputation contributes to build and maintain trust in their products, which in turn influences the GPs’ prescribing intents. PCs need to acknowledge that corporate reputation is a multi-dimensional construct and should focus their efforts accordingly. Indeed, our study shows that GPs’ favourable perception of the PCs’ medical representatives (MRs) has a strong impact on their drug prescribing intents. An investment in corporate social responsibility (CSR) would, therefore, be conducive to increasing a PCs’ corporate reputation capital. We constructed and tested a conceptual model to explain GPs’ prescribing intents by highlighting the influential relationships between different non-pharmaceutical variables. Our conceptual model integrates marketing concepts, such as consumer behaviour, the drug prescribing intention of GPs, as well as specific public relations concepts, corporate reputation, and corporate social responsibility.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".